Stimulation Modes to Adapt Customized Stimulation Parameters for Use in a Spinal Cord Stimulation System

ABSTRACT

A method is disclosed for programming a patient&#39;s stimulator device using an external device. The method provides a Graphical User Interface (GUI) on the external device that allows the patient to select from a plurality of displayed stimulation modes to program stimulation provided by one or more electrodes of the stimulator device. The external device stores a model derived for the patient, which model comprises information indicative of a plurality of frequency/pulse width/amplitude coordinates predicted to provide optimal stimulation for the patient. Each stimulation mode corresponds with a subset of coordinates defined in accordance with the plurality of coordinates in the model. Selection of one of the stimulation modes limits programming the stimulator device with coordinates that are within the corresponding subset of coordinates.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a continuation of U.S. patent application Ser. No. 16/460,655,filed Jul. 2, 2019, which is a non-provisional application of U.S.Provisional Patent Application Ser. No. 62/803,330, filed Feb. 8, 2019.U.S. patent application Ser. No. 16/460,655 is also acontinuation-in-part of U.S. patent application Ser. No. 16/100,904,filed Aug. 10, 2018 (now U.S. Pat. No. 10,576,282), which is anon-provisional application of U.S. Provisional Patent Application Ser.Nos. 62/693,543, filed Jul. 3, 2018, which is incorporated by reference,and 62/544,656, filed Aug. 11, 2017. Priority is claimed to theseapplications.

FIELD OF THE INVENTION

This application relates to Implantable Medical Devices (IMDs),generally, Spinal Cord Stimulators, more specifically, and to methods ofcontrol of such devices.

INTRODUCTION

Implantable neurostimulator devices are devices that generate anddeliver electrical stimuli to body nerves and tissues for the therapy ofvarious biological disorders, such as pacemakers to treat cardiacarrhythmia, defibrillators to treat cardiac fibrillation, cochlearstimulators to treat deafness, retinal stimulators to treat blindness,muscle stimulators to produce coordinated limb movement, spinal cordstimulators to treat chronic pain, cortical and deep brain stimulatorsto treat motor and psychological disorders, and other neural stimulatorsto treat urinary incontinence, sleep apnea, shoulder subluxation, etc.The description that follows will generally focus on the use of theinvention within a Spinal Cord Stimulation (SCS) system, such as thatdisclosed in U.S. Pat. No. 6,516,227. However, the present invention mayfind applicability with any implantable neurostimulator device system.

An SCS system typically includes an Implantable Pulse Generator (IPG) 10shown in FIG. 1. The IPG 10 includes a biocompatible device case 12 thatholds the circuitry and battery 14 necessary for the IPG to function.The IPG 10 is coupled to electrodes 16 via one or more electrode leads15 that form an electrode array 17. The electrodes 16 are configured tocontact a patient's tissue and are carried on a flexible body 18, whichalso houses the individual lead wires 20 coupled to each electrode 16.The lead wires 20 are also coupled to proximal contacts 22, which areinsertable into lead connectors 24 fixed in a header 23 on the IPG 10,which header can comprise an epoxy for example. Once inserted, theproximal contacts 22 connect to header contacts within the leadconnectors 24, which are in turn coupled by feedthrough pins through acase feedthrough to circuitry within the case 12, although these detailsaren't shown.

In the illustrated IPG 10, there are sixteen lead electrodes (E1-E16)split between two leads 15, with the header 23 containing a 2×1 array oflead connectors 24. However, the number of leads and electrodes in anIPG is application specific and therefore can vary. The conductive case12 can also comprise an electrode (Ec). In a SCS application, theelectrode leads 15 are typically implanted proximate to the dura in apatient's spinal column on the right and left sides of the spinal cordmidline. The proximal electrodes 22 are tunneled through the patient'stissue to a distant location such as the buttocks where the IPG case 12is implanted, at which point they are coupled to the lead connectors 24.In other IPG examples designed for implantation directly at a siterequiring stimulation, the IPG can be lead-less, having electrodes 16instead appearing on the body of the IPG for contacting the patient'stissue. The IPG leads 15 can be integrated with and permanentlyconnected the case 12 in other IPG solutions. The goal of SCS therapy isto provide electrical stimulation from the electrodes 16 to alleviate apatient's symptoms, most notably chronic back pain.

IPG 10 can include an antenna 26 a allowing it to communicatebi-directionally with a number of external devices, as shown in FIG. 4.The antenna 26 a as depicted in FIG. 1 is shown as a conductive coilwithin the case 12, although the coil antenna 26 a can also appear inthe header 23. When antenna 26 a is configured as a coil, communicationwith external devices preferably occurs using near-field magneticinduction. IPG may also include a Radio-Frequency (RF) antenna 26 b. InFIG. 1, RF antenna 26 b is shown within the header 23, but it may alsobe within the case 12. RF antenna 26 b may comprise a patch, slot, orwire, and may operate as a monopole or dipole. RF antenna 26 bpreferably communicates using far-field electromagnetic waves. RFantenna 26 b may operate in accordance with any number of known RFcommunication standards, such as Bluetooth, Zigbee, WiFi, MICS, and thelike.

Stimulation in IPG 10 is typically provided by pulses, as shown in FIG.2. Stimulation parameters typically include the amplitude of the pulses(A; whether current or voltage); the frequency (F) and pulse width (PW)of the pulses; the electrodes 16 (E) activated to provide suchstimulation; and the polarity (P) of such active electrodes, i.e.,whether active electrodes are to act as anodes (that source current tothe tissue) or cathodes (that sink current from the tissue). Thesestimulation parameters taken together comprise a stimulation programthat the IPG 10 can execute to provide therapeutic stimulation to apatient.

In the example of FIG. 2, electrode E5 has been selected as an anode,and thus provides pulses which source a positive current of amplitude +Ato the tissue. Electrode E4 has been selected as a cathode, and thusprovides pulses which sink a corresponding negative current of amplitude−A from the tissue. This is an example of bipolar stimulation, in whichonly two lead-based electrodes are used to provide stimulation to thetissue (one anode, one cathode). However, more than one electrode mayact as an anode at a given time, and more than one electrode may act asa cathode at a given time (e.g., tripole stimulation, quadripolestimulation, etc.).

The pulses as shown in FIG. 2 are biphasic, comprising a first phase 30a, followed quickly thereafter by a second phase 30 b of oppositepolarity. As is known, use of a biphasic pulse is useful in activecharge recovery. For example, each electrodes' current path to thetissue may include a serially-connected DC-blocking capacitor, see,e.g., U.S. Patent Application Publication 2016/0144183, which willcharge during the first phase 30 a and discharged (be recovered) duringthe second phase 30 b. In the example shown, the first and second phases30 a and 30 b have the same duration and amplitude (although oppositepolarities), which ensures the same amount of charge during both phases.However, the second phase 30 b may also be charged balance with thefirst phase 30 a if the integral of the amplitude and durations of thetwo phases are equal in magnitude, as is well known. The width of eachpulse, PW, is defined here as the duration of first pulse phase 30 a,although pulse width could also refer to the total duration of the firstand second pulse phases 30 a and 30 b as well. Note that an interphaseperiod (IP) during which no stimulation is provided may be providedbetween the two phases 30 a and 30 b.

IPG 10 includes stimulation circuitry 28 that can be programmed toproduce the stimulation pulses at the electrodes as defined by thestimulation program. Stimulation circuitry 28 can for example comprisethe circuitry described in U.S. Patent Application Publications2018/0071513 and 2018/0071520, or described in U.S. Pat. Nos. 8,606,362and 8,620,436. These references are incorporated herein by reference.

FIG. 3 shows an external trial stimulation environment that may precedeimplantation of an IPG 10 in a patient. During external trialstimulation, stimulation can be tried on a prospective implant patientwithout going so far as to implant the IPG 10. Instead, one or moretrial leads 15′ are implanted in the patient's tissue 32 at a targetlocation 34, such as within the spinal column as explained earlier. Theproximal ends of the trial lead(s) 15′ exit an incision 36 and areconnected to an External Trial Stimulator (ETS) 40. The ETS 40 generallymimics operation of the IPG 10, and thus can provide stimulation pulsesto the patient's tissue as explained above. See, e.g., U.S. Pat. No.9,259,574, disclosing a design for an ETS. The ETS 40 is generally wornexternally by the patient for a short while (e.g., two weeks), whichallows the patient and his clinician to experiment with differentstimulation parameters to try and find a stimulation program thatalleviates the patient's symptoms (e.g., pain). If external trialstimulation proves successful, trial lead(s) 15′ are explanted, and afull IPG 10 and lead(s) 15 are implanted as described above; ifunsuccessful, the trial lead(s) 15′ are simply explanted.

Like the IPG 10, the ETS 40 can include one or more antennas to enablebi-directional communications with external devices, explained furtherwith respect to FIG. 4. Such antennas can include a near-fieldmagnetic-induction coil antenna 42 a, and/or a far-field RF antenna 42b, as described earlier. ETS 40 may also include stimulation circuitry44 able to form the stimulation pulses in accordance with a stimulationprogram, which circuitry may be similar to or comprise the samestimulation circuitry 28 present in the IPG 10. ETS 40 may also includea battery (not shown) for operational power.

FIG. 4 shows various external devices that can wirelessly communicatedata with the IPG 10 and the ETS 40, including a patient, hand-heldexternal controller 45, and a clinician programmer 50. Both of devices45 and 50 can be used to send a stimulation program to the IPG 10 or ETS40—that is, to program their stimulation circuitries 28 and 44 toproduce pulses with a desired shape and timing described earlier. Bothdevices 45 and 50 may also be used to adjust one or more stimulationparameters of a stimulation program that the IPG 10 or ETS 40 iscurrently executing. Devices 45 and 50 may also receive information fromthe IPG 10 or ETS 40, such as various status information, etc.

External controller 45 can be as described in U.S. Patent ApplicationPublication 2015/0080982 for example, and may comprise either adedicated controller configured to work with the IPG 10. Externalcontroller 45 may also comprise a general purpose mobile electronicsdevice such as a mobile phone which has been programmed with a MedicalDevice Application (MDA) allowing it to work as a wireless controllerfor the IPG 10 or ETS 40, as described in U.S. Patent ApplicationPublication 2015/0231402. External controller 45 includes a userinterface, including means for entering commands (e.g., buttons oricons) and a display 46. The external controller 45's user interfaceenables a patient to adjust stimulation parameters, although it may havelimited functionality when compared to the more-powerful clinicianprogrammer 50, described shortly.

The external controller 45 can have one or more antennas capable ofcommunicating with the IPG 10 and ETS 40. For example, the externalcontroller 45 can have a near-field magnetic-induction coil antenna 47 acapable of wirelessly communicating with the coil antenna 26 a or 42 ain the IPG 10 or ETS 40. The external controller 45 can also have afar-field RF antenna 47 b capable of wirelessly communicating with theRF antenna 26 b or 42 b in the IPG 10 or ETS 40.

The external controller 45 can also have control circuitry 48 such as amicroprocessor, microcomputer, an FPGA, other digital logic structures,etc., which is capable of executing instructions an electronic device.Control circuitry 48 can for example receive patient adjustments tostimulation parameters, and create a stimulation program to bewirelessly transmitted to the IPG 10 or ETS 40.

Clinician programmer 50 is described further in U.S. Patent ApplicationPublication 2015/0360038, and is only briefly explained here. Theclinician programmer 50 can comprise a computing device 51, such as adesktop, laptop, or notebook computer, a tablet, a mobile smart phone, aPersonal Data Assistant (PDA)-type mobile computing device, etc. In FIG.4, computing device 51 is shown as a laptop computer that includestypical computer user interface means such as a screen 52, a mouse, akeyboard, speakers, a stylus, a printer, etc., not all of which areshown for convenience. Also shown in FIG. 4 are accessory devices forthe clinician programmer 50 that are usually specific to its operationas a stimulation controller, such as a communication “wand” 54, and ajoystick 58, which are coupleable to suitable ports on the computingdevice 51, such as USB ports 59 for example.

The antenna used in the clinician programmer 50 to communicate with theIPG 10 or ETS 40 can depend on the type of antennas included in thosedevices. If the patient's IPG 10 or ETS 40 includes a coil antenna 26 aor 42 a, wand 54 can likewise include a coil antenna 56 a to establishnear-filed magnetic-induction communications at small distances. In thisinstance, the wand 54 may be affixed in close proximity to the patient,such as by placing the wand 54 in a belt or holster wearable by thepatient and proximate to the patient's IPG 10 or ETS 40.

If the IPG 10 or ETS 40 includes an RF antenna 26 b or 42 b, the wand54, the computing device 51, or both, can likewise include an RF antenna56 b to establish communication with the IPG 10 or ETS 40 at largerdistances. (Wand 54 may not be necessary in this circumstance). Theclinician programmer 50 can also establish communication with otherdevices and networks, such as the Internet, either wirelessly or via awired link provided at an Ethernet or network port.

To program stimulation programs or parameters for the IPG 10 or ETS 40,the clinician interfaces with a clinician programmer graphical userinterface (GUI) 64 provided on the display 52 of the computing device51. As one skilled in the art understands, the GUI 64 can be rendered byexecution of clinician programmer software 66 on the computing device51, which software may be stored in the device's non-volatile memory 68.One skilled in the art will additionally recognize that execution of theclinician programmer software 66 in the computing device 51 can befacilitated by control circuitry 70 such as a microprocessor,microcomputer, an FPGA, other digital logic structures, etc., which iscapable of executing programs in a computing device. Such controlcircuitry 70, in addition to executing the clinician programmer software66 and rendering the GUI 64, can also enable communications via antennas56 a or 56 b to communicate stimulation parameters chosen through theGUI 64 to the patient's IPG 10.

A portion of the GUI 64 is shown in one example in FIG. 5. One skilledin the art will understand that the particulars of the GUI 64 willdepend on where clinician programmer software 66 is in its execution,which will depend on the GUI selections the clinician has made. FIG. 5shows the GUI 64 at a point allowing for the setting of stimulationparameters for the patient and for their storage as a stimulationprogram. To the left a program interface 72 is shown, which as explainedfurther in the '038 publication allows for naming, loading and saving ofstimulation programs for the patient. Shown to the right is astimulation parameters interface 82, in which specific stimulationparameters (A, D, F, E, P) can be defined for a stimulation program.Values for stimulation parameters relating to the shape of the waveform(A; in this example, current), pulse width (PW), and frequency (F) areshown in a waveform parameter interface 84, including buttons theclinician can use to increase or decrease these values.

Stimulation parameters relating to the electrodes 16 (the electrodes Eactivated and their polarities P), are made adjustable in an electrodeparameter interface 86. Electrode stimulation parameters are alsovisible and can be manipulated in a leads interface 92 that displays theleads 15 (or 15′) in generally their proper position with respect toeach other, for example, on the left and right sides of the spinalcolumn. A cursor 94 (or other selection means such as a mouse pointer)can be used to select a particular electrode in the leads interface 92.Buttons in the electrode parameter interface 86 allow the selectedelectrode (including the case electrode, Ec) to be designated as ananode, a cathode, or off. The electrode parameter interface 86 furtherallows the relative strength of anodic or cathodic current of theselected electrode to be specified in terms of a percentage, X. This isparticularly useful if more than one electrode is to act as an anode orcathode at a given time, as explained in the '038 Publication. Inaccordance with the example waveforms shown in FIG. 2, as shown in theleads interface 92, electrode E5 has been selected as the only anode tosource current, and this electrode receives X=100% of the specifiedanodic current, +A. Likewise, electrode E4 has been selected as the onlycathode to sink current, and this electrode receives X=100% of thatcathodic current, −A.

The GUI 64 as shown specifies only a pulse width PW of the first pulsephase 30 a. The clinician programmer software 66 that runs and receivesinput from the GUI 64 will nonetheless ensure that the IPG 10 and ETS 40are programmed to render the stimulation program as biphasic pulses ifbiphasic pulses are to be used. For example, the clinician programmingsoftware 66 can automatically determine durations and amplitudes forboth of the pulse phases 30 a and 30 b (e.g., each having a duration ofPW, and with opposite polarities +A and −A). An advanced menu 88 canalso be used (among other things) to define the relative durations andamplitudes of the pulse phases 30 a and 30 b, and to allow for othermore advance modifications, such as setting of a duty cycle (on/offtime) for the stimulation pulses, and a ramp-up time over whichstimulation reaches its programmed amplitude (A), etc. A mode menu 90allows the clinician to choose different modes for determiningstimulation parameters. For example, as described in the '038Publication, mode menu 90 can be used to enable electronic trolling,which comprises an automated programming mode that performs currentsteering along the electrode array by moving the cathode in a bipolarfashion.

While GUI 64 is shown as operating in the clinician programmer 50, theuser interface of the external controller 45 may provide similarfunctionality.

SUMMARY

In one example, a method disclosed for programming a patient'sstimulator device, which may comprise: providing a Graphical UserInterface (GUI) that allows the patient to select from a plurality ofdisplayed stimulation modes to program stimulation provided by one ormore electrodes of the stimulator device; storing information indicativeof a plurality of subsets of stimulation parameters derived for thepatient, wherein each stimulation mode corresponds to one of the subsetsof stimulation parameters; and based on selection of one of thestimulation modes, limiting programming the stimulator device tostimulation parameters that are within the corresponding subset ofstimulation parameters.

In one example, each subset of stimulation parameters comprises at leasttwo of a frequency, a pulse width, and an amplitude. In one example, thestimulation parameters in each subset comprise a line in amulti-dimensional space of at least two of frequency, pulse width, andamplitude. In one example, the stimulation parameters in each subsetcomprise a volume in a multi-dimensional space of at least two offrequency, pulse width, and amplitude. In one example, the stimulationparameters of the selected stimulation mode are configured to providesub-perception stimulation for the patient. In one example, thestimulation parameters of the selected stimulation mode are configuredto provide supra-perception stimulation for the patient. In one example,at least one of the stimulation modes is indicative of a posture oractivity of the patient. In one example, at least one of the stimulationmodes is indicative of a power mode for the stimulator device. In oneexample, the method further comprises providing on the GUI an automaticoption that allows the stimulator device to detect when at least one ofthe stimulation modes should be entered, wherein detection of one of thestimulation modes by the stimulator device limits programming thestimulator device with stimulation parameters that are within thecorresponding subset of stimulation parameters for the detected one ofthe stimulation modes. In one example, the GUI permits the patient toselect the at least one stimulation mode to be detected. In one example,the stimulator device includes at least one sensor for detecting whenthe at least one of the stimulation modes is to be entered. In oneexample, the at least one sensor comprises an accelerometer. In oneexample, the at least one sensor comprises a clock. In one example, theat least one sensor comprises a sensor that detects a voltage of abattery in the stimulator device. In one example, the at least onesensor comprises at least one of the electrodes of the stimulatordevice. In one example, the method further comprises providing on theGUI an automatic option that allows an external device to detect when atleast one of the stimulation modes should be entered, wherein detectionof one of the stimulation modes by the external device limits theexternal device to programming the stimulator device with stimulationparameters that are within the corresponding subset of stimulationparameters for the detected one of the stimulation modes. In oneexample, the external device is configured to detect when the at leastone of the stimulation modes is to be entered by receiving informationfrom another device. In one example, the method further comprisesproviding on the GUI one or more options to allow the patient to programthe stimulator device by selecting stimulation parameters that arewithin the subset of stimulation parameters corresponding with theselected stimulation mode. In one example, at least one of the one ormore options allows the patient to adjust at least two of a frequency,pulse width, and amplitude of the stimulation parameters with which thestimulator device is programmed. In one example, the subsets ofstimulation parameters are derived for the patient using measurementstaken from the patient in response to providing stimulation to thepatient during a testing procedure. In one example, the GUI is providedon a patient external controller, and further comprising programming theplurality of displayed stimulation modes using a clinician programmer.

In one example, a system is disclosed, which may comprise: a stimulatordevice configured for implantation in a patient comprising a pluralityof electrodes; and an external device configured to program thestimulator device with stimulation to be provided at one or more of theplurality of electrodes, wherein the external device stores informationindicative of a plurality of subsets of stimulation parameters derivedfor the patient; wherein the external device is configured to: provide aGraphical User Interface (GUI) configured to allow the patient to selectfrom a plurality of displayed stimulation modes to program thestimulation, wherein each stimulation mode corresponds to one of thesubsets of stimulation parameters, and based on selection of one of thestimulation modes, limit programming the stimulator device tostimulation parameters that are within the corresponding subset ofstimulation parameters.

In one example, each subset of stimulation parameters comprises at leasttwo of a frequency, a pulse width, and an amplitude. In one example, thestimulation parameters in each subset comprise a line in amulti-dimensional space of at least two of frequency, pulse width, andamplitude. In one example, the stimulation parameters in each subsetcomprise a volume in a multi-dimensional space of at least two offrequency, pulse width, and amplitude. In one example, the stimulationparameters of the selected stimulation mode are configured to providesub-perception stimulation for the patient. In one example, thestimulation parameters of the selected stimulation mode are configuredto provide supra-perception stimulation for the patient. In one example,at least one of the stimulation modes is indicative of a posture oractivity of the patient. In one example, at least one of the stimulationmodes is indicative of a power mode for the stimulator device. In oneexample, the external device is further configured to provide on the GUIan automatic option configured to allow the stimulator device to detectwhen at least one of the stimulation modes should be entered, whereinthe external device is configured upon detection of one of thestimulation modes by the stimulator device to limit programming thestimulator device with stimulation parameters that are within thecorresponding subset of stimulation parameters for the detected one ofthe stimulation modes. In one example, the GUI is configured to permitthe patient to select the at least one stimulation mode to be detected.In one example, the stimulator device includes at least one sensor fordetecting when the at least one of the stimulation modes is to beentered. In one example, the at least one sensor comprises anaccelerometer. In one example, the at least one sensor comprises aclock. In one example, the at least one sensor comprises a sensor thatdetects a voltage of a battery in the stimulator device. In one example,the at least one sensor comprises at least one of the electrodes of thestimulator device. In one example, the external device is furtherconfigured to provide on the GUI an automatic option configured to allowthe external device to detect when at least one of the stimulation modesshould be entered, wherein the external device is configured upondetection of one of the stimulation modes to limit programming thestimulator device with stimulation parameters that are within thecorresponding subset of stimulation parameters for the detected one ofthe stimulation modes. In one example, the external device is configuredto detect when the at least one of the stimulation modes is to beentered by receiving information from another device. In one example,the external device is further configured to provide on the GUI one ormore options configured to allow the patient to program the stimulatordevice by selecting stimulation parameters that are within the subset ofstimulation parameters corresponding with the selected stimulation mode.In one example, at least one of the one or more options is configured toallow the patient to adjust at least two of a frequency, pulse width,and amplitude of the stimulation parameters with which the stimulatordevice is programmed. In one example, the subsets of stimulationparameters are derived for the patient using measurements taken from thepatient in response to providing stimulation to the patient during atesting procedure. In one example, the system further comprises aclinician programmer, wherein the clinician programmer is configured toprogram the plurality of displayed stimulation modes in the externaldevice.

In one example, a non-transitory computer readable medium is disclosedconfigured for operation in an external device configured to program astimulator device implantable in a patient with stimulation to beprovided at one or more of the plurality of electrodes, the mediumincluding information indicative of a plurality of subsets ofstimulation parameters derived for the patient, wherein the mediumincludes instructions that, when executed on the external device, may beconfigured to: provide a Graphical User Interface (GUI) on the externaldevice that allows the patient to select from a plurality of displayedstimulation modes to program the stimulation, wherein each stimulationmode corresponds to one of the subsets of stimulation parameters derivedfor the patient, and based on selection of one of the stimulation modes,limit programming the stimulator device to stimulation parameters thatare within the corresponding subset of stimulation parameters.

In one example, a method is disclosed for programming a patient'sstimulator device, which may comprise: determining a model for thepatient, wherein the model comprises information indicative of predictedstimulation parameters useable for the patient; determining informationindicative of a plurality of subsets of stimulation parameters using themodel, wherein each subset corresponds with one of a plurality ofstimulation modes; and providing a Graphical User Interface (GUI) toallow the patient to select from the plurality of stimulation modes,wherein selection of one of the stimulation modes limits programming thestimulator device to stimulation parameters that are within thecorresponding subset of stimulation parameters.

In one example, the stimulation parameters in each subset comprise aline or volume in a multi-dimensional space of at least two offrequency, pulse width, and amplitude. In one example, the model isdetermined for the patient using measurements taken from the patient inresponse to providing stimulation to the patient during a testingprocedure. In one example, the stimulation is provided to the patientduring the testing procedure at different pulse widths, and wherein themeasurements comprise an indication of a perception threshold at eachpulse width, thereby determining a relationship between pulse width andperception threshold for the patient. In one example, the model isdetermined by comparing the relationship to another model to determinethe predicted stimulation parameters in the model, wherein the anothermodel comprises a relationship between frequency, pulse width, andperception threshold. In one example, the model and the plurality ofsubsets are determined in a clinician programmer in communication withthe stimulator device, and further comprising transmitting thedetermined plurality of subsets from the clinician programmer to anexternal device. In one example, the model is determined in a clinicianprogrammer in communication with the stimulator device, and furthercomprising transmitting the model to an external device, wherein theplurality of subsets are determined in the external device. In oneexample, the predicted stimulation parameters in the model comprise aline or volume in a multi-dimensional space of at least two offrequency, pulse width, and amplitude. In one example, at least onesubset is determined using the model such that the stimulationparameters of the at least one subset are wholly constrained by thepredicted stimulation parameters in the model. In one example, at leastone subset is determined using the model such that the stimulationparameters of the at least one subset are partially constrained by thepredicted stimulation parameters in the model. In one example, thepredicted stimulation parameters in the model comprises stimulationparameters predicted to provide sub-perception stimulation for thepatient. In one example, the model further comprises informationindicative of the patient's paresthesia threshold, wherein at least onestimulation mode provides supra-perception stimulation for the patientby stimulation parameters in the corresponding subset in which anamplitude stimulation parameter exceeds the paresthesia threshold. Inone example, the stimulation parameters of the selected stimulation modeare configured to provide sub-perception stimulation for the patient. Inone example, the stimulation parameters of the selected stimulation modeare configured to provide supra-perception stimulation for the patient.In one example, at least one of the stimulation modes is indicative of aposture or activity of the patient. In one example, at least one of thestimulation modes is indicative of a power mode for the stimulatordevice. In one example, the method further comprises providing on theGUI an automatic option that allows for detection when at least one ofthe stimulation modes should be entered, wherein detection of one of thestimulation modes limits programming the stimulator device withstimulation parameters that are within the corresponding subset ofstimulation parameters for the detected one of the stimulation modes. Inone example, the method further comprises providing on the GUI one ormore options to allow the patient to program the stimulator device byselecting stimulation parameters that are within the subset ofstimulation parameters corresponding with the selected stimulation mode.In one example, at least one of the one or more options allows thepatient to adjust at least two of a frequency, pulse width, andamplitude of the parameters to which the stimulator device isprogrammed. In one example, the stimulation parameters in at least oneof the subsets is adjustable. In one example, the GUI is provided on apatient external controller, and further comprising programming theplurality of stimulation modes using a clinician programmer.

In one example, a system is disclosed, which may comprise: a stimulatordevice configured for implantation in a patient comprising a pluralityof electrodes; and at least one external device configured to determinea model for the patient, wherein the model comprises informationindicative of predicted stimulation parameters useable for the patient;determine information indicative of a plurality of subsets ofstimulation parameters using the model, wherein each subset correspondswith one of a plurality of stimulation modes; and provide a GraphicalUser Interface (GUI) configured to allow the patient to select from theplurality of stimulation modes, wherein the at least one external deviceis configured, based on selection of one of the stimulation modes, tolimit programming the stimulator device to stimulation parameters thatare within the corresponding subset of stimulation parameters.

In one example, the stimulation parameters in each subset comprise aline or volume in a multi-dimensional space of at least two offrequency, pulse width, and amplitude. In one example, the at least oneexternal device is configured to determine the model for the patient byreceiving measurements taken from the patient in response to providingstimulation to the patient during a testing procedure. In one example,the at least one external device is configured to provide stimulation tothe patient during the testing procedure at different pulse widths, andwherein the measurements comprise an indication of a perceptionthreshold at each pulse width, wherein the at least one external deviceis configured to determine a relationship between pulse width andperception threshold for the patient. In one example, the at least oneexternal device is configured to determine the model for the patient bycomparing the relationship to another model to determine the predictedstimulation parameters in the model, wherein the another model comprisesa relationship between frequency, pulse width, and perception threshold.In one example, the at least one external device comprises a clinicianprogrammer and a patient external controller, wherein the clinicianprogrammer is configured to determine the model and the informationindicative of the plurality of subsets, wherein the clinician programmeris configured to transmit the determined plurality of subsets from theclinician programmer to the patient external controller. In one example,the at least one external device comprises a clinician programmer and apatient external controller, wherein the clinician programmer isconfigured to determine the model and to transmit the model to thepatient external controller, wherein the patient external controller isconfigured to determine the information indicative of the plurality ofsubsets. In one example, the at least one external device comprises aclinician programmer and a patient external controller, wherein theclinician programmer is configured to program the plurality ofstimulation modes in the patient external controller having the GUI. Inone example, the predicted stimulation parameters in the model comprisea line or volume in a multi-dimensional space of at least two offrequency, pulse width, and amplitude. In one example, the at least oneexternal device is configured to determine at least one of the subsetsusing the model such that the stimulation parameters of the at least onesubset are wholly constrained by the predicted stimulation parameters inthe model. In one example, the at least one external device isconfigured to determine at least one of the subsets using the model suchthat the stimulation parameters of the at least one subset are partiallyconstrained by the predicted stimulation parameters in the model. In oneexample, the predicted stimulation parameters in the model comprisesstimulation parameters predicted to provide sub-perception stimulationfor the patient. In one example, the model further comprises informationindicative of the patient's paresthesia threshold, wherein at least onestimulation mode provides supra-perception stimulation for the patientby stimulation parameters in the corresponding subset in which anamplitude stimulation parameter exceeds the paresthesia threshold. Inone example, the stimulation parameters of the selected stimulation modeare configured to provide sub-perception stimulation for the patient. Inone example, the stimulation parameters of the selected stimulation modeare configured to provide supra-perception stimulation for the patient.In one example, at least one of the stimulation modes is indicative of aposture or activity of the patient. In one example, at least one of thestimulation modes is indicative of a power mode for the stimulatordevice. In one example, the at least one external device is configuredto provide on the GUI an automatic option configured to allow fordetection when at least one of the stimulation modes should be entered,wherein the at least one external device is configured to limitprogramming the stimulator device to stimulation parameters that arewithin the corresponding subset of stimulation parameters for thedetected one of the stimulation modes. In one example, the at least oneexternal device is configured to provide on the GUI one or more optionsto allow the patient to program the stimulator device by selectingstimulation parameters that are within the subset of stimulationparameters corresponding with the selected stimulation mode. In oneexample, at least one of the one or more options allows the patient toadjust at least two of a frequency, pulse width, and amplitude of theparameters to which the stimulator device is programmed. In one example,the at least one external device is configured to allow a user to adjustthe stimulation parameters in at least one of the subsets.

In one example, at least one non-transitory computer readable medium isdisclosed configured for operation in at least one external deviceconfigured to program a stimulator device implantable in a patient withstimulation to be provided at one or more of the plurality ofelectrodes, wherein the at least one medium includes instructions that,when executed on the at least one external device, may be configured to:determine a model for the patient, wherein the model comprisesinformation indicative of predicted stimulation parameters useable forthe patient; determine information indicative of a plurality of subsetsof stimulation parameters using the model, wherein each subsetcorresponds with one of a plurality of stimulation modes; and provide aGraphical User Interface (GUI) configured to allow the patient to selectfrom the plurality of stimulation modes, wherein the at least oneexternal device is configured, based on selection of one of thestimulation modes, to limit programming the stimulator device tostimulation parameters that are within the corresponding subset ofstimulation parameters.

In one example, a method is disclosed for programming a patient'sstimulator device using an external device, which may comprise:providing a Graphical User Interface (GUI) on the external device thatallows the patient to select from a plurality of displayed stimulationmodes to program stimulation provided by one or more electrodes of thestimulator device, wherein the external device stores informationindicative of a plurality of subsets of coordinates, wherein eachcoordinate in each subset comprises stimulation parameters derived forthe patient to provide optimal stimulation for that patient, whereineach stimulation mode corresponds with one of the subsets ofcoordinates, wherein selection of one of the stimulation modes limitsprogramming the stimulator device with coordinates that are within thecorresponding subset of coordinates.

In one example, each coordinate comprises a frequency, a pulse width,and an amplitude. In one example, the coordinates in each subsetcomprises a line in a three-dimensional space of frequency, pulse width,and amplitude. In one example, the coordinates in each subset comprisesa volume in a three-dimensional space of frequency, pulse width, andamplitude. In one example, the method may further comprise determining amodel for the patient, wherein the model comprises informationindicative of a plurality of coordinates, wherein each coordinate in themodel comprises stimulation parameters predicted to provide optimalstimulation for the patient, wherein the plurality of subsets ofcoordinates are determined using the model. In one example, the model isdetermined for the patient using measurements taken from the patient inresponse to providing stimulation to the patient during a testingprocedure. In one example, the stimulation is provided to the patientduring the testing procedure at different pulse widths, and wherein themeasurements comprise an indication of a perception threshold at eachpulse width, thereby determining a relationship between pulse width andperception threshold for the patient. In one example, the perceptionthresholds comprise a lowest amplitude of the stimulation pulses atwhich a patient can perceive the stimulation pulses. In one example, themodel is determined by comparing the relationship to another model todetermine the plurality of coordinates in the model. In one example, theanother model comprises a relationship between frequency, pulse width,and perception threshold. In one example, the model and the plurality ofsubsets are determined in a clinician programmer in communication withthe stimulator device. In one example, the method further comprisestransmitting the determined plurality of subsets from the clinicianprogrammer to the external device. In one example, the model isdetermined in a clinician programmer in communication with thestimulator device, further comprising transmitting the model to theexternal device, wherein the plurality of subsets are determined in theexternal device. In one example, the plurality of coordinates in themodel comprises a line in a three-dimensional space of frequency, pulsewidth, and amplitude. In one example, the plurality of coordinates inthe model comprises a volume in a three-dimensional space of frequency,pulse width, and amplitude. In one example, at least one subset ofcoordinates is determined using the model such that the coordinates ofthe at least one subset are wholly constrained by the plurality ofcoordinates in the model. In one example, at least one subset ofcoordinates is determined using the model such that the coordinates ofthe at least one subset are partially constrained by the plurality ofcoordinates in the model. In one example, the at least one subset ispartially constrained by the plurality of coordinates in the model suchthat only some of the stimulation parameters for the coordinates in theat least one subset equal the stimulation parameters of coordinateswithin the model, but at least one of the stimulation parameters for thecoordinates in the at least one subset is outside the stimulationparameters of coordinates within the model. In one example, eachcoordinate in the model comprises stimulation parameters predicted toprovide optimal sub-perception stimulation for the patient. In oneexample, the model further comprises information indicative of thepatient's paresthesia threshold at each of the plurality of coordinates,wherein at least one stimulation mode provides supra-perception for thepatient by providing coordinates in the corresponding subset in which anamplitude stimulation parameter exceeds the paresthesia threshold. Inone example, at least one of the stimulation modes is configured toprovide sub-perception stimulation for the patient. In one example, atleast one of the stimulation modes is configured to providesupra-perception stimulation for the patient. In one example, at leastone of the stimulation modes is indicative of a posture or activity ofthe patient. In one example, at least one of the stimulation modes isindicative of a power mode for the stimulator device. In one example,the method further comprises providing on the GUI an automatic optionthat allows the stimulator device to detect when at least one of thestimulation modes should be entered, wherein detection of one of thestimulation modes by the stimulator device limits the external device toprogramming the stimulator device with coordinates that are within thecorresponding subset of coordinates for the detected one of thestimulation modes. In one example, the GUI permits the patient to selectthe at least one stimulation mode that should be detected. In oneexample, the stimulator device includes at least one sensor fordetecting when the at least one of the stimulation modes should beentered. In one example, the at least one sensor comprises anaccelerometer. In one example, the at least one sensor comprises aclock. In one example, the at least one sensor comprises a sensor thatdetects a voltage of a battery in the stimulator device. In one example,the at least one sensor comprises at least one of the electrodes of thestimulator device. In one example, the method further comprisesproviding on the GUI an automatic option that allows the external deviceto detect when at least one of the stimulation modes should be entered,wherein detection of one of the stimulation modes by the external devicelimits the external device to programming the stimulator device withcoordinates that are within the corresponding subset of coordinates forthe detected one of the stimulation modes. In one example, the externaldevice detects when the at least one of the stimulation modes should beentered by receiving information from another device. In one example,the method further comprises providing on the user interface one or moreoptions to allow the patient to program the stimulator device byselecting coordinates that are within the subset of coordinatescorresponding with the selected stimulation mode. In one example, atleast one of the one or more options allows the patient tosimultaneously adjust a frequency, pulse width, and amplitude of thecoordinates to which the stimulator device is programmed. In oneexample, the subsets of coordinates are derived for the patient usingmeasurements taken from the patient in response to providing stimulationto the patient during a testing procedure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an Implantable Pulse Generator (IPG) useable for SpinalCord Stimulation (SC S), in accordance with the prior art.

FIG. 2 shows an example of stimulation pulses producible by the IPG, inaccordance with the prior art.

FIG. 3 shows use of an External Trial Stimulator (ETS) useable toprovide stimulation before implantation of an IPG, in accordance withthe prior art.

FIG. 4 shows various external devices capable of communicating with andprogramming stimulation in an IPG and ETS, in accordance with the priorart.

FIG. 5 shows a Graphical User Interface (GUI) of a clinician programmerexternal device for setting or adjusting stimulation parameters, inaccordance with the prior art.

FIG. 6 shows sweet spot searching to determine effective electrodes fora patient using a movable sub-perception bipole.

FIGS. 7A-7D show sweet spot searching to determine effective electrodesfor a patient using a movable supra-perception bipole.

FIG. 8 shows stimulation circuitry useable in the IPG or ETS capable ofproviding Multiple Independent Current Control to independently set thecurrent at each of the electrodes.

FIG. 9 shows a flow chart of a study conducted on various patients withback pain designed to determine optimal sub-perception SCS stimulationparameters over a frequency range of 1 kHz to 10 kHz.

FIGS. 10A-10C show various results of the study as a function ofstimulation frequency in the 1 kHz to 10 kHz frequency range, includingaverage optimal pulse width (FIG. 10A), mean charge per second andoptimal stimulation amplitude (FIG. 10B), and back pain scores (FIG.10C).

FIGS. 11A-11C shows further analysis of relationships between averageoptimal pulse width and frequency in the 1 kHz to 10 kHz frequencyrange, and identifies statistically-significant regions of optimizationof these parameters.

FIG. 12A shows results of patients tested with sub-perception therapy atfrequencies at or below 1 kHz, and shows optimal pulse width rangesdetermined at tested frequencies, and optimal pulse width v. frequencyregions for sub-perception therapy.

FIG. 12B shows various modelled relationships between average optimalpulse width and frequency at or below 1 kHz.

FIG. 12C shows the duty of cycle of the optimal pulse widths as afunction of frequencies at or below 1 kHz.

FIG. 12D shows the average battery current and battery discharge time atthe optimal pulse widths as a function of frequencies at or below 1 kHz.

FIGS. 13A and 13B shows the results of additional testing that verifiesthe frequency versus pulse width relationships presented earlier.

FIG. 14 shows a fitting module showing how the relationships and regionsdetermined relating optimal pulse width and frequency (≤10 kHz) can beused to set sub-perception stimulation parameters for an IPG or ETS.

FIG. 15 shows an algorithm used for supra-perception sweet spotsearching followed by sub-perception therapy, and possible optimizationof the sub-perception therapy using the fitting module.

FIG. 16 shows an alternative algorithm for optimization of thesub-perception therapy using the fitting module.

FIG. 17 shows a model derived from patients showing a surface denotingoptimal sub-perception values for frequency and pulse width, and furtherincluding the patients' perception threshold pth as measured at thosefrequencies and pulse widths.

FIGS. 18A and 18B show the perception threshold pth plotted versus pulsewidth for a number of patients, and shows how results can be curve fit.

FIG. 19 shows a graph of parameter Z versus pulse width for patients,where Z comprises an optimal amplitude A for patients expressed as apercentage of perception threshold pth (i.e., Z=A/pth).

FIGS. 20A-20F show an algorithm used to derive a range of optimalsub-perception stimulation parameters (e.g., F, PW, and A) for a patientusing the modelling information of FIGS. 17-19, and using perceptionthreshold measurements taken on the patient.

FIG. 21 shows use of the optimal stimulation parameters in a patientexternal controller, including a user interface that allows the patientto adjust stimulation within the range.

FIGS. 22A-22F show the effect of statistic variance in the modelling,leading to the result that determined optimal stimulation parameters fora patient may occupy a volume. User interfaces for the patient externalcontroller are also shown to allow the patient to adjust stimulationwithin this volume.

FIG. 23 shows a stimulation mode user interface, from which a patientmay select different stimulation modes, resulting in providingstimulation, or allowing the patient to control stimulation, usingdifferent subsets of stimulation parameters determined using the optimalstimulation parameters.

FIGS. 24A-29B show examples of different subsets of stimulationparameters based on the patient's selection of different stimulationmodes. Figures labeled A (e.g., 24A) show frequencies and pulse widthsof a subset, while figures labeled B (e.g., 24B) show amplitudes andperception thresholds for that subset. These figures show that subsetsof stimulation parameters corresponding to different stimulation modesmay comprise parameters wholly constrained by (i.e., wholly within) thedetermined optimal stimulation parameters, or may comprise parametersonly partially constrained by the optimal stimulation parameters.

FIG. 30 shows an automatic mode in which the IPG and/or externalcontroller are used to determine when particular stimulation modesshould automatically be entered based on sensed information.

FIG. 31 shows another example of a simulation mode user interface, inwhich stimulation modes are presented for selection on a two-dimensionalrepresentation of stimulation parameters, although a three-dimensionalrepresentation indicative of subset volume can also be used.

FIG. 32 shows GUI aspects that allows a patient to adjust stimulation,where a suggested stimulation region for the patient is shown inconjunction with adjustment aspects.

DETAILED DESCRIPTION

While Spinal Cord Stimulation (SCS) therapy can be an effective means ofalleviating a patient's pain, such stimulation can also causeparesthesia. Paresthesia—sometimes referred to a “supra-perception”therapy—is a sensation such as tingling, prickling, heat, cold, etc.that can accompany SCS therapy. Generally, the effects of paresthesiaare mild, or at least are not overly concerning to a patient. Moreover,paresthesia is generally a reasonable tradeoff for a patient whosechronic pain has now been brought under control by SCS therapy. Somepatients even find paresthesia comfortable and soothing.

Nonetheless, at least for some patients, SCS therapy would ideallyprovide complete pain relief without paresthesia—what is often referredto as “sub-perception” or sub-threshold therapy that a patient cannotfeel. Effective sub-perception therapy may provide pain relief withoutparesthesia by issuing stimulation pulses at higher frequencies.Unfortunately, such higher-frequency stimulation may require more power,which tends to drain the battery 14 of the IPG 10. See, e.g., U.S.Patent Application Publication 2016/0367822. If an IPG's battery 14 is aprimary cell and not rechargeable, high-frequency stimulation means thatthe IPG 10 will need to be replaced more quickly. Alternatively, if anIPG battery 14 is rechargeable, the IPG 10 will need to be charged morefrequently, or for longer periods of time. Either way, the patient isinconvenienced.

In an SCS application, it is desirable to determine a stimulationprogram that will be effective for each patient. A significant part ofdetermining an effective stimulation program is to determine a “sweetspot” for stimulation in each patient, i.e., to select which electrodesshould be active (E) and with what polarities (P) and relativeamplitudes (X %) to recruit and thus treat a neural site at which painoriginates in a patient. Selecting electrodes proximate to this neuralsite of pain can be difficult to determine, and experimentation istypically undertaken to select the best combination of electrodes toprovide a patient's therapy.

As described in U.S. Pat. No. 11,160,987, which is hereby expresslyincorporated by reference, selecting electrodes for a given patient canbe even more difficult when sub-perception therapy is used, because thepatient does not feel the stimulation, and therefore it can be difficultfor the patient to feel whether the stimulation is “covering” his painand therefore whether selected electrodes are effective. Further,sub-perception stimulation therapy may require a “wash in” period beforeit can become effective. A wash in period can take up to a day or more,and therefore sub-perception stimulation may not be immediatelyeffective, making electrode selection more difficult.

FIG. 6 briefly explains the '987 patent's technique for a sweet spotsearch, i.e., how electrodes can be selected that are proximate to aneural site of pain 298 in a patient, when sub-perception stimulation isused. The technique of FIG. 6 is particularly useful in a trial settingafter a patient is first implanted with an electrode array, i.e., afterreceiving their IPG or ETS.

In the example shown, it is assumed that a pain site 298 is likelywithin a tissue region 299. Such region 299 may be deduced by aclinician based on the patient symptoms, e.g., by understanding whichelectrodes are proximate to certain vertebrae (not shown), such aswithin the T9-T10 interspace. In the example shown, region 299 isbounded by electrodes E2, E7, E15, and E10, meaning that electrodesoutside of this region (e.g., E1, E8, E9, E16) are unlikely to have aneffect on the patient's symptoms. Therefore, these electrodes may not beselected during the sweet spot search depicted in FIG. 6, as explainedfurther below.

In FIG. 6, a sub-perception bipole 297 a is selected, in which oneelectrode (e.g., E2) is selected as an anode that will source a positivecurrent (+A) to the patient's tissue, while another electrode (e.g., E3)is selected as a cathode that will sink a negative current (−A) from thetissue. This is similar to what was illustrated earlier with respect toFIG. 2, and biphasic stimulation pulses can be used employing activecharge recovery. Because the bipole 297 a provides sub-perceptionstimulation, the amplitude A used during the sweet spot search istitrated down until the patient no longer feels paresthesia. Thissub-perception bipole 297 a is provided to the patient for a duration,such as a few days, which allows the sub-perception bipole's potentialeffectiveness to “wash in,” and allows the patient to provide feedbackconcerning how well the bipole 297 a is helping their symptoms. Suchpatient feedback can comprise a pain scale ranking. For example, thepatient can rank their pain on a scale from 1-10 using a NumericalRating Scale (NRS) or the Visual Analogue Scale (VAS), with 1 denotingno or little pain and 10 denoting a worst pain imaginable. As discussedin the '987 patent, such pain scale ranking can be entered into thepatient's external controller 45.

After the bipole 297 a is tested at this first location, a differentcombination of electrodes is chosen (anode electrode E3, cathodeelectrode E4), which moves the location of the bipole 297 in thepatient's tissue. Again, the amplitude of the current A may need to betitrated to an appropriate sub-perception level. In the example shown,the bipole 297 a is moved down one electrode lead, and up the other, asshown by path 296 in the hope of finding a combination of electrodesthat covers the pain site 298. In the example of FIG. 6, given the painsite 298's proximity to electrodes E13 and E14, it might be expectedthat a bipole 297 a at those electrodes will provide the best relief forthe patient, as reflected by the patient's pain score rankings. Theparticular stimulation parameters chosen when forming bipole 297 a canbe selected at the GUI 64 of the clinician programmer 50 or otherexternal device (such as a patient external controller 45) andwirelessly telemetered to the patient's IPG or ETS for execution.

While the sweet spot search of FIG. 6 can be effective, it can also takea significantly long time when sub-perception stimulation is used. Asnoted, sub-perception stimulation is provided at each bipole 297location for a number of days, and because a large number of bipolelocations are chosen, the entire sweep spot search can take up to amonth to complete.

The inventors have determined via testing of SCS patients that even ifit is desired to eventually use sub-perception therapy for a patientgoing forward after the sweet spot search, it is beneficial to usesupra-perception stimulation during the sweet spot search to selectactive electrodes for the patient. Use of supra-perception stimulationduring the sweet spot search greatly accelerates determination ofeffective electrodes for the patient compared to the use ofsub-perception stimulation, which requires a wash in period at each setof electrodes tested. After determining electrodes for use with thepatient using supra-perception therapy, therapy may be titrated tosub-perception levels keeping the same electrodes determined for thepatient during the sweet spot search. Because the selected electrodesare known to be recruiting the neural site of the patient's pain, theapplication of sub-perception therapy to those electrodes is more likelyto have immediate effect, reducing or potentially eliminating the needto wash in the sub-perception therapy that follows. In short, effectivesub-perception therapy can be achieved more quickly for the patient whensupra-perception sweet spot searching is utilized. Preferably,supra-perception sweet spot searching occurs using symmetric biphasicpulses occurring at low frequencies—such as between 40 and 200 Hz in oneexample.

In accordance with one aspect of the disclosed technique, a patient willbe provided sub-perception therapy. Sweet spot searching to determineelectrodes that may be used during sub-perception therapy may precedesuch sub-perception therapy. In some aspects, when sub-perceptiontherapy is used for the patient, sweet spot searching may use a bipole297 a that is sub-perception (FIG. 6), as just described. This may berelevant because the sub-perception sweet spot search may match theeventual sub-perception therapy the patient will receive.

However, the inventors have determined that even if sub-perceptiontherapy is eventually to be used for the patient, it can be beneficialto use supra-perception stimulation—that is, stimulation withaccompanying paresthesia—during the sweet spot search. This is shown inFIG. 7A, where the movable bipole 301 a provides supra-perceptionstimulation that can be felt by the patient. Providing bipole 301 a assupra-perception stimulation can merely involve increasing its amplitude(e.g., current A) when compared to the sub-perception bipole 297 a ofFIG. 6, although other stimulation parameters might be adjusted as well,such as by providing longer pulse widths.

The inventors have determined that there are benefits to employingsupra-perception stimulation during the sweet spot search even thoughsub-perception therapy will eventually be used for the patient.

First, as mentioned above, the use of supra-perception therapy bydefinition allows the patient to feel the stimulation, which enables thepatient to provide essentially immediate feedback to the clinicianwhether the paresthesia seems to be well covering his pain site 298. Inother words, it is not necessary to take the time to wash in bipole 301a at each location as it is moved along path 296. Thus, a suitablebipole 301 a proximate to the patient's pain site 298 can be establishedmuch more quickly, such as within a single clinician's visit, ratherthan over a period of days or weeks. In one example, when sub-perceptiontherapy is preceded with supra-perception sweet spot searching, the timeneeded to wash in the sub-perception therapy can be one hour or less,ten minutes or less, or even a matter of seconds. This allows wash in tooccur during a single programming session during which the patient's IPGor ETS is programmed, and without the need for the patient to leave theclinician's office.

Second, use of supra-perception stimulation during the sweet spot searchensures that electrodes are determined that well recruit the pain site298. As a result, after the sweet spot search is complete and eventualsub-perception therapy is titrated for the patient, wash in of thatsub-perception therapy may not take as long because the electrodesneeded for good recruitment have already been confidently determined.

FIGS. 7B-7D show other supra-perception bipoles 301 b-301 d that may beused, and in particular show how the virtual bipoles may be formed usingvirtual poles by activating three or more of the electrodes 16. Virtualpoles are discussed further in U.S. Patent Application Publication2019/0175915, which is incorporated herein by reference in its entirety,and thus virtual poles are only briefly explained here. Forming virtualpoles is assisted if the stimulation circuitry 28 or 44 used in the IPGor ETS is capable of independently setting the current at any of theelectrodes—what is sometimes known as a Multiple Independent CurrentControl (MICC), which is explained further below with reference to FIG.8.

When a virtual bipole is used, the GUI 64 (FIG. 5) of the clinicianprogrammer 50 (FIG. 4) can be used to define an anode pole (+) and acathode pole (−) at positions 291 (FIG. 7B) that may not necessarilycorrespond to the position of the physical electrodes 16. The controlcircuitry 70 in the clinician programmer 50 can compute from thesepositions 291 and from other tissue modeling information which physicalelectrodes 16 will need to be selected and with what amplitudes to formthe virtual anode and virtual cathode at the designated positions 291.As described earlier, amplitudes at selected electrodes may be expressedas a percentage X % of the total current amplitude A specified at theGUI 64 of the clinician programmer 50.

For example, in FIG. 7B, the virtual anode pole is located at a position291 between electrodes E2, E3 and E10. The clinician programmer 50 maythen calculate based on this position that each of these electrodes(during first pulse phase 30 a) will receive an appropriate share (X %)of the total anodic current +A to locate the virtual anode at thisposition. Since the virtual anode's position is closest to electrode E2,this electrode E2 may receive the largest share of the specified anodiccurrent +A (e.g., 75%*+A). Electrodes E3 and E10 which are proximate tothe virtual anode pole's position but farther away receive lesser sharesof the anodic current (e.g., 15%*+A and 10%*+A respectively). Likewise,it can be seen that from the designated position 291 of the virtualcathode pole, which is proximate to electrodes E4, E11, and E12, thatthese electrodes will receive an appropriate share of the specifiedcathodic current −A (e.g., 20%*−A, 20%*−A, and 60%*−A respectively,again during the first pulse phase 30 a). These polarities would then beflipped during the second phases 30 b of the pulses, as shown in thewaveforms of FIG. 7B. In any event, the use of virtual poles in theformation of bipole 301 b allows the field in the tissue to be shaped,and many different combinations of electrodes can be tried during thesweet spot search. In this regard, it is not strictly necessary that the(virtual) bipole be moved along an orderly path 296 with respect to theelectrodes, and the path may be randomized, perhaps as guided byfeedback from the patient.

FIG. 7C shows a useful virtual bipole 301 c configuration that can beused during the sweet spot search. This virtual bipole 301 c againdefines a target anode and cathode whose positions do not correspond tothe position of the physical electrodes. The virtual bipole 301 c isformed along a lead—essentially spanning the length of four electrodesfrom E1 to E5. This creates a larger field in the tissue better able torecruit the patient's pain site 298. This bipole configuration 301 c mayneed to be moved to a smaller number of locations than would a smallerbipole configuration compared 301 a of FIG. 7A) as it moves along path296, thus accelerating pain site 298 detection. FIG. 7D expands upon thebipole configuration of FIG. 7C to create a virtual bipole 301 d usingelectrodes formed on both leads, e.g., from electrodes E1 to E5 and fromelectrodes E9 to E13. This bipole 301 d configuration need only be movedalong a single path 296 that is parallel to the leads, as its field islarge enough to recruit neural tissue proximate to both leads. This canfurther accelerate pain site detection.

In some aspects, the supra-perception bipoles 301 a-301 d used duringthe sweet spot search comprise symmetric biphasic waveforms havingactively-driven (e.g., by the stimulation circuitry 28 or 44) pulsephases 30 a and 30 b of the same pulse width PW and the same amplitude(with the polarity flipped during the phases) (e.g., A_(30a)=A_(30b),and PW_(30a)=PW_(30b)). This is beneficial because the second pulsephase 30 b provides active charge recovery, with in this case the chargeprovided during the first pulse phase 30 a (Q_(30a)) equaling the chargeof the second pulse phase 30 b (Q_(30b)), such that the pulses arecharge balanced. Use of biphasic waveforms are also believed beneficialbecause, as is known, the cathode is largely involved in neural tissuerecruitment. When a biphasic pulse is used, the positions of the(virtual) anode and cathode will flip during the pulse's two phases.This effectively doubles the neural tissue that is recruited forstimulation, and thus increases the possibility that the pain site 298will be covered by a bipole at the correct location.

The supra-perception bipoles 301 a-301 d do not however need to comprisesymmetric biphasic pulses as just described. For example, the amplitudeand pulse width of the two phases 30 a and 30 b can be different, whilekeeping the charge (Q) of the two phases balanced (e.g.,Q_(30a)=A_(30a)*PW_(30a)=A_(30b)*PW_(30b)=Q_(30b)). Alternatively, thetwo phases 30 a and 30 b may be charge imbalanced (e.g.,Q_(30a)=A_(30a)*PW_(30a)>A_(30b)*PW_(30b)=Q_(30b), orQ_(30a)=A_(30a)*PW_(30a)<A_(30b)*PW_(30b)=Q_(30b)). In short, the pulsesin bipoles 301-301 d can be biphasic symmetric (and thus inherentlycharge balanced), biphasic asymmetric but still charge balanced, orbiphasic asymmetric and charge imbalanced.

In a preferred example, the frequency F of the supra-perception pulses301 a-301 d used during the supra-perception sweet spot search may be 10kHz or less, 1 kHz or less, 500 Hz or less, 300 Hz or less, 200 Hz orless, 130 Hz or less, or 100 Hz or less, or ranges bounded by two ofthese frequencies (e.g., 100-130 Hz, or 100-200 Hz). In particularexamples, frequencies of 90 Hz, 40 Hz, or 10 Hz can be used, with pulsescomprising biphasic pulses which are preferably symmetric. However, asingle actively-driven pulse phase followed by a passive recovery phasecould also be used. The pulse width PW may also comprise a value in therange of hundreds of microseconds, such as 150 to 400 microseconds.Because the goal of supra-perception sweet spot searching is merely todetermine electrodes that appropriately cover a patient's pain,frequency and pulse width may be of less importance at this stage. Onceelectrodes have been chosen for sub-perception stimulation, frequencyand pulse width can be optimized, as discussed further below.

It should be understood that the supra-perception bipoles 301 a-301 dused during sweet spot searching need not necessarily be the sameelectrodes that are selected when later providing the patient withsub-perception therapy. Instead, the best location of the bipole noticedduring the search can be used as the basis to modify the selectedelectrodes. Suppose for example that a bipole 301 a (FIG. 7A) is usedduring sweep spot searching, and it is determined that bipole providesthe best pain relief when located at electrodes E13 and E14. At thatpoint, sub-perception therapy using those electrodes E13 and E14 can betried for the patient going forward. Alternatively, it may be sensibleto modify the selected electrodes to see if the patient's symptoms canbe further improved before sub-perception therapy is tried. For example,the distance (focus) between the cathode and anode can be varied, usingvirtual poles as already described. Or, a tripole (anode/cathode/anode)consisting of electrodes E12/E13/E14 or E13/E14/E15 could be tried. SeeU.S. Patent Application Publication 2019/0175915 (discussing tripoles).Or electrodes on a different lead could also be tried in combinationwith E13 and E14. For example, because electrodes E5 and E6 aregenerally proximate to electrodes E13 and E14, it may be useful to addE5 or E6 as sources of anodic or cathodic current (again creatingvirtual poles). All of these types of adjustments should be understoodas comprising “steering” or an adjustment to the “location” at whichtherapy is applied, even if a central point of stimulation doesn'tchange (as can occur for example when the distance or focus between thecathode and anode is varied).

Multiple Independent Current Control (MICC) is explained in one examplewith reference to FIG. 8, which shows the stimulation circuitry 28(FIG. 1) or 44 (FIG. 3) in the IPG or ETS used to form prescribedstimulation at a patient's tissue. The stimulation circuitry 28 or 44can control the current or charge at each electrode independently, andusing GUI 64 (FIG. 5) allows the current or charge to be steered todifferent electrodes, which is useful for example when moving the bipole301 i along path 296 during the sweet spot search (FIG. 7A-7D). Thestimulation circuitry 28 or 44 includes one or more current sources 440_(i), and one or more current sinks 442 k. The sources and sinks 440_(i), and 442 _(i), can comprise Digital-to-Analog converters (DACs),and may be referred to as PDACs 440 _(i), and NDACs 442 _(i), inaccordance with the Positive (sourced, anodic) and Negative (sunk,cathodic) currents they respectively issue. In the example shown, aNDAC/PDAC 440 _(i)/442 _(i) pair is dedicated (hardwired) to aparticular electrode node ei 39. Each electrode node ei 39 is preferablyconnected to an electrode Ei 16 via a DC-blocking capacitor Ci 38, whichact as a safety measure to prevent DC current injection into thepatient, as could occur for example if there is a circuit fault in thestimulation circuitry 28 or 44. PDACs 440 _(i), and NDACs 442 _(i), canalso comprise voltage sources.

Proper control of the PDACs 440 _(i), and NDACs 442 _(i), via GUI 64allows any of the electrodes 16 and the case electrode Ec 12 to act asanodes or cathodes to create a current through a patient's tissue. Suchcontrol preferably comes in the form of digital signals Tip and Iin thatset the anodic and cathodic current at each electrode Ei. If for exampleit is desired to set electrode E1 as an anode with a current of +3 mA,and to set electrodes E2 and E3 as cathodes with a current of −1.5 mAeach, control signal Ilp would be set to the digital equivalent of 3 mAto cause PDAC 440 ₁ to produce +3 mA, and control signals I2 n and I3 nwould be set to the digital equivalent of 1.5 mA to cause NDACs 4422 and4423 to each produce −1.5 mA. Note that definition of these controlsignals can also occur using the programmed amplitude A and percentage X% set in the GUI 64. For example, A may be set to 3 mA, with E1designated as an anode with X=100%, and with E2 and E3 designated atcathodes with X=50%. Alternatively, the control signals may not be setwith a percentage, and instead the GUI 64 can simply prescribe thecurrent that will appear at each electrode at any point in time.

In short, the GUI 64 may be used to independently set the current ateach electrode, or to steer the current between different electrodes.This is particularly useful in forming virtual bipoles, which asexplained earlier involve activation of more than two electrodes. MICCalso allows more sophisticated electric fields to be formed in thepatient's tissue.

Other stimulation circuitries 28 can also be used to implement MICC. Inan example not shown, a switching matrix can intervene between the oneor more PDACs 440 _(i) and the electrode nodes ei 39, and between theone or more NDACs 442 _(i) and the electrode nodes. Switching matricesallows one or more of the PDACs or one or more of the NDACs to beconnected to one or more electrode nodes at a given time. Variousexamples of stimulation circuitries can be found in U.S. Pat. Nos.6,181,969, 8,606,362, 8,620,436, and U.S. Patent ApplicationPublications 2018/0071513, 2018/0071520, and 2019/0083796.

Much of the stimulation circuitry 28 or 44, including the PDACs 440 _(i)and NDACs 442 _(i), the switch matrices (if present), and the electrodenodes ei 39 can be integrated on one or more Application SpecificIntegrated Circuits (ASICs), as described in U.S. Patent ApplicationPublications 2012/0095529, 2012/0092031, and 2012/0095519. As explainedin these references, ASIC(s) may also contain other circuitry useful inthe IPG 10, such as telemetry circuitry (for interfacing off chip withthe IPG's or ETS's telemetry antennas), circuitry for generating thecompliance voltage VH that powers the stimulation circuitry, variousmeasurement circuits, etc.

While it is preferred to use sweet spot searching, and in particularsupra-perception sweet spot searching, to determine the electrodes to beused during subsequent sub-perception therapy, it should be noted thatthis is not strictly necessary. Sub-perception therapy can be precededby sub-perception sweet spot searching, or may not be preceded by sweetspot searching at all. In short, sub-perception therapy as describednext is not reliant on the use of any sweet spot search.

In another aspect of the invention, the inventors have determined viatesting of SCS patients that statistically significant correlationsexists between pulse width (PW) and frequency (F) where an SCS patientwill experience a reduction in back pain without paresthesia(sub-perception). Use of this information can be helpful in decidingwhat pulse width is likely optimal for a given SCS patient based on aparticular frequency, and in deciding what frequency is likely optimalfor a given SCS patient based on a particular pulse width. Beneficially,this information suggests that paresthesia-free sub-perception SCSstimulation can occur at frequencies of 10 kHz and below. Use of suchlow frequencies allows sub-perception therapy to be used with much lowerpower consumption in the patient's IPG or ETS.

FIGS. 9-11C shows results derived from testing patients at frequencieswithin a range of 1 kHz to 10 kHz. FIG. 9 explains how data was gatheredfrom actual SCS patients, and the criteria for patient inclusion in thestudy. Patients with back pain, but not yet receiving SCS therapy, werefirst identified. Key patient inclusion criteria included havingpersistent lower back pain for greater than 90 days; a NRS pain scale of5 or greater (NRS is explained below); stable opioid medications for 30days; and a Baseline Oswestry Disability index score of greater than orequal to 20 and lower than or equal to 80. Key patient exclusioncriteria included having back surgery in the previous 6 months;existence of other confounding medical/psychological conditions; anduntreated major psychiatric comorbidity or serious drug related behaviorissues.

After such initial screening, patients periodically entered aqualitative indication of their pain (i.e., a pain score) into aportable e-diary device, which can comprise a patient externalcontroller 45, and which in turn can communicate its data to a clinicianprogrammer 50 (FIG. 4). Such pain scores can comprise a Numerical RatingScale (NRS) score from 1-10, and were input to the e-diary three timesdaily. As shown in FIG. 10C, the baseline NRS score for patients noteventually excluded from the study and not yet receiving sub-perceptionstimulation therapy was approximately 6.75/10, with a standard error, SE(sigma/SQRT(n)) of 0.25.

Returning to FIG. 9, patients then had trial leads 15′ (FIG. 3)implanted on the left and right sides of the spinal column, and wereprovided external trial stimulation as explained earlier. A clinicianprogrammer 50 was used to provide a stimulation program to eachpatient's ETS 40 as explained earlier. This was done to make sure thatSCS therapy was helpful for a given patient to alleviate their pain. IfSCS therapy was not helpful for a given patient, trial leads 15′ wereexplanted, and that patient was then excluded from the study.

Those patients for whom external trial stimulation was helpfuleventually received full implantation of a permanent IPG 10, asdescribed earlier. After a healing period, and again using clinicianprogrammer 50, a “sweet spot” for stimulation was located in eachpatient, i.e., which electrodes should be active (E) and with whatpolarities (P) and relative amplitudes (X %) to recruit and thus treat asite 298 of neural site in the patient. The sweet spot search can occurin any of the manners described earlier with respect to FIGS. 6-7D, butin a preferred embodiment would comprise supra-perception stimulation(e.g., e.g., 7A-7D) because of the benefits described earlier. However,this is not strictly necessary, and sub-perception stimulation can alsobe used during the sweet spot search. In the example of FIG. 9, sweetspot searching occurred at 10 kHz, but again the frequency used duringthe sweet spot search can be varied. Symmetric biphasic pulses were usedduring sweet spot searching, but again, this is not strictly required.Deciding which electrodes should be active started with selectingelectrodes 16 present between thoracic vertebrae T9 and T10. However,electrodes as far away as T8 and T11 were also activated if necessary.Which electrodes were proximate to vertebrae T8, T9, T10, and T1 wasdetermined using fluoroscopic images of the leads 15 within eachpatient.

During sweet spot searching, bipolar stimulation using only twoelectrodes was used for each patient, and using only adjacent electrodeson a single lead 15, similar to what was described in FIGS. 6 and 7A.Thus, one patient's sweet spot might involve stimulating adjacentelectrodes E4 as cathode and E5 as anode on the left lead 15 as shownearlier in FIG. 2 (which electrodes may be between T9 and T10), whileanother patient's sweet spot might involve stimulating adjacentelectrodes E9 as anode and E10 as cathode on the right lead 15 (whichelectrodes may be between T10 and T11). Using only adjacent-electrodebipolar stimulation and only between vertebrae T8 to T11 was desired tominimize variance in the therapy and pathology between the differentpatients in the study. However, more complicated bipoles such as thosedescribed with respect to FIGS. 7B-7D could also be used during sweetspot searching. If a patient had sweet spot electrodes in the desiredthoracic location, and if they experienced a 30% or greater pain reliefper an NRS score, such patients were continued in the study; patientsnot meeting these criteria were excluded from further study. While thestudy started initially with 39 patients, 19 patients were excluded fromstudy up to this point in FIG. 9, leaving a total of 20 patientsremaining.

The remaining 20 patients were then subjected to a “washout” period,meaning their IPGs did not provide stimulation for a time. Specifically,patients' NRS pain scores were monitored until their pain reached 80% oftheir initial baseline pain. This was to ensure that previous benefitsof stimulation did not carry over to a next analysis period.

Thereafter, remaining patients were subjected to sub-perception SCStherapy at different frequencies in the range from 1 kHz to 10 kHz usingthe sweet spot active electrodes determined earlier. This however isn'tstrictly necessary, because as noted earlier the current at eachelectrode could also be independently controlled to assist in shaping ofthe electric filed in the tissue. As shown in FIG. 9, the patients wereeach tested using stimulation pulses with frequencies of 10 kHz, 7 kHz,4 kHz, and 1 kHz. FIG. 9 for simplicity shows that these frequencieswere tested in this order for each patient, but in reality thefrequencies were applied to each patient in random orders. Testing at agiven frequency, once complete, was followed by a washout period beforetesting at another frequency began.

At each tested frequency, the amplitude (A) and pulse width (PW) (firstpulse phase 30 a; FIG. 2) of the stimulation was adjusted and optimizedfor each patient such that each patient experienced good pain reliefpossible but without paresthesia (sub-perception). Specifically, usingclinician programmer 50, and keeping as active the same sweet spotelectrodes determined earlier (although again this isn't strictlynecessary), each patient was stimulated at a low amplitude (e.g., 0),which amplitude was increased to a maximum point (perception threshold)where paresthesia was noticeable by the patient. Initial stimulation wasthen chosen for the patient at 50% of that maximum amplitude, i.e., suchthat stimulation was sub-perception and hence paresthesia free. However,other percentages of the maximum amplitude (80%, 90%, etc.) could bechosen as well, and can vary with patient activity or position, asexplained further below. In one example, the stimulation circuitry 28 or44 in the IPG or ETS is configurable to receive an instruction from theGUI 64 via a selectable option (not shown) to reduce the amplitude ofthe stimulation pulses to or by a set amount or percentage to render theso that the pulses can be made sub-perception if they are not already.Other stimulation parameters may also be reduced (e.g., pulse width,charge) to the same effect.

The patient would then leave the clinician's office, and thereafter andin communication with the clinician (or her technician or programmer)would make adjustments to his stimulation (amplitude and pulse width)using his external controller 45 (FIG. 4). At the same time, the patientwould enter NRS pain scores in his e-diary (e.g., the externalcontroller), again three times a day. Patient adjustment of theamplitude and pulse width was typically an iterative process, butessentially adjustments were attempted based on feedback from thepatient to adjust the therapy to decrease their pain while stillensuring that stimulation was sub-perception. Testing at each frequencylasted about three weeks, and stimulation adjustments might be madeevery couple of days or so. At the end of the testing period at a givenfrequency, optimal amplitude and pulse widths had been determined andwere logged for each patient, along with patient NRS pain scores forthose optimal parameters as entered in their e-diaries.

In one example, the percentage of the maximum amplitude used to providesub-perception stimulation could be chosen dependent on an activitylevel or position of the patient. In regard, the IPG or ETS can includemeans for determining patient activity or position, such as anaccelerometer. If the accelerometer indicates a high degree of patientactivity or a position where the electrodes would be farther away fromthe spinal cord (e.g., lying down), the amplitude could be increased toa higher percentage to increase the current (e.g., 90% of the maximumamplitude). If the patient is experiencing a lower degree of activity ora position where the electrodes would be closer to the spinal card(e.g., standing), the amplitude can be decreased (e.g., to 50% of themaximum amplitude). Although not shown, the GUI 64 of the externaldevice (FIG. 5) can include an option to set the percentage of themaximum amplitude at which paresthesia become noticeable to the patient,thus allowing the patient to adjust the sub-perception currentamplitude.

Preferably, Multiple Independent Current Control (MICC) is used toprovide or adjust the sub-perception therapy, as discussed earlier withreference to FIG. 8. This allows the current at each electrode to beindependently set, which promotes the steering of current or chargebetween electrodes, facilitates the formation of virtual bipoles, andmore generally allows the electric field to be shaped in the patient'stissue. In particular, MICC, can be used to steer sub-perception therapyto different locations in the electrode array and thus the spinal cord.For example, once a set of sub-perception stimulation parameters hasbeen chosen for the patient, one or more of the stimulation parameterscan be changed. Such changes may be warranted or dictated by the therapylocation. The physiology of the patient may vary at different vertebralpositions, and tissue may be more or less conductive at differenttherapy locations. Therefore, if the sub-perception therapy location issteered to a new location along the spinal cord (which location changemay comprise changing the anode/cathode distance or focus), it may bewarranted to adjust at least one of the stimulation parameters, such asamplitude. As noted earlier, making sub-perception adjustment isfacilitated, and can occur within a programming session, because asubstantial wash in period may not be necessary.

Adjustment to sub-perception therapy can also include varying otherstimulation parameters, such as pulse width, frequency, and even theduration of the interphase period (IP) (FIG. 2). The interphase durationcan impact the neural dose, or the rate of charge infusion, such thathigher sub-perception amplitudes would be used with shorter interphasedurations. In one example, the interphase duration can be varied between0-3 ms. After a washout period, a new frequency was tested, using thesame protocol as just described.

The sub-perception stimulation pulses used were symmetric biphasicconstant current amplitude pulses, having first and second pulses phases30 a and 30 b with the same duration (see FIG. 2). However, constantvoltage amplitude pulses could be used as well. Pulses of differentshapes (triangles, sine waves, etc.) could also be used.Pre-pulsing—that is, providing a small current prior to providing theactively-driven pulse phase(s)—to affect polarization or depolarizationof neural tissue can also occur when providing sub-perception therapy.See, e.g., U.S. Pat. No. 9,008,790, which is incorporated herein byreference.

FIGS. 10A-10C show the results of testing the patients at 10 kHz, 7 kHz,4 Hz and 1 kHz. Data is shown in each figure as average values for the20 remaining patients at each frequency, with error bars reflectingstandard error (SE) between the patients.

Starting with FIG. 10B, the optimized amplitude A for the 20 remainingpatients are shown at the tested frequencies. Interestingly, the optimalamplitude at each frequency was essentially constant—around 3 mA. FIG.10B also shows the amount of energy expended at each frequency, morespecifically a mean charge per second (MCS) (in mC/s) attributable tothe pulses. MCS is computed by taking the optimal pulse width (FIG. 10A,discussed next) and multiplying it by the optimal amplitude (A) and thefrequency (F), which MCS value can comprise a neural dose. MCScorrelates to the current or power that the battery in the IPG 10 mustexpend to form the optimal pulses. Significantly, the MCS issignificantly lower at lower frequencies: for example, the MCS at F=1kHz is approximately ⅓ of its value at higher frequencies (e.g., F=7 kHzor 10 kHz). This means that optimal SCS therapy—that alleviates backpain without paresthesia—is achievable at lower frequencies like F=1kHz, with the added benefit of lower power draws that are moreconsiderate of the IPG 10's (or ETS 40's) battery.

FIG. 10A shows optimal pulse width as a function of frequency for the 1kHz to 10 kHz frequency range tested. As shown, the relationship followsa statistically significant trend: when modeled using linear regression98 a, PW=−8.22 F+106, where pulse width is measured in microseconds andfrequency is measured in kiloHertz, with a correlation coefficient R² of0.974; when modeled using polynomial regression 98 b, PW=0.486 F²−13.6F+116, again with pulse width measured in microseconds and frequencymeasured in kiloHertz, with an even better correlation coefficient ofR²=0.998. Other fitting methods could be used to establish otherinformation relating frequency and pulse width at which stimulationpulses are formed to provide pain relief without paresthesia in thefrequency range of 1 kHz to 10 kHz.

Note that the relationship between optimal pulse width and frequency isnot simply an expected relationship between frequency and duty cycle(DC), i.e., the duration that a pulse is ‘on’ divided by its period(1/F). In this regard, notice that a given frequency has a naturaleffect on pulse width: one would expect that a higher frequency pulseswould have smaller pulse widths. Thus, it might be expected for examplethat a 1 kHz waveform with a 100 microsecond pulse width would have thesame clinical results as a 10 kHz waveform with a 10 microsecondfrequency, because the duty cycle of both of these waveforms is 10%.FIG. 11A shows the resulting duty cycle of the stimulation waveformsusing the optimal pulse width in the frequency range of 1 kHz to 10 kHz.Here, duty cycle is computed by considering the total ‘on’ time of thefirst pulse phase 30 a (FIG. 2) only; the duration of the symmetricsecond pulse phase is ignored. This duty cycle is not constant over the1 kHz to 10 kHz frequency range: for example, the optimal pulse width at1 kHz (104 microseconds) is not merely ten times the optimal pulse widthat 10 kHz (28.5 microseconds). Thus, there is significance to theoptimal pulse widths beyond a mere scaling of the frequency.

FIG. 10C shows average patient pain scores at the optimal stimulationparameters (optimal amplitude (FIG. 7B) and pulse width (FIG. 7A)) foreach frequency in the range of 1 kHz to 10 kHz. As noted earlier,patients in the study, prior to receiving SCS therapy, initiallyreported pain scores with an average of 6.75. After SCS implantation andduring the study, and with amplitude and pulse width optimized duringthe provisional of sub-perception therapy, their average pain scoresdropped significantly, to an average score of about 3 for allfrequencies tested.

FIG. 11A provides a deeper analysis of the resulting relationshipbetween optimal pulse width and frequency in the frequency range of 1kHz to 10 kHz. The chart in FIG. 11A shows the average optimal pulsewidth for the 20 patients in the study at each frequency, along with thestandard error resulting from variations between them. These arenormalized at each frequency by dividing the standard error by theoptimal pulse width, ranging in variations at each frequency between5.26% and 8.51%. From this, a 5% variance (lower than all computedvalues) can be assumed as a statistically-significant variance at allfrequencies tested.

From this 5% variance, a maximum average pulse width (PW+5%) and aminimum average pulse width (PW+5%) can be calculated for eachfrequency. For example, the optimal average pulse width PW at 1 kHz is104 microseconds, and 5% above this value (1.05*104 μs) is 109 μs; 5%below this value (0.95*104) is 98.3 μs. Likewise, the optimal averagepulse width AVG(PW) at 4 kHz is 68.0 microseconds, and 5% above thisvalue (1.05*68.0 μs) is 71.4 μs; 5% below this value (0.95*68.0 μs) is64.6 μs. Thus, a statistically-significant reduction in pain withoutparesthesia occurs in or on the linearly bounded region 100 a of points102 of (1 kHz, 98.3 μs), (1 kHz, 109 μs), (4 kHz, 71.4 μs), and (4 kHz,64.6 μs). A linearly bounded region 100 b around points 102 is alsodefined for frequencies greater than or equal to 4 kHz and less than orequal to 7 kHz: (4 kHz, 71.4 μs), (4 kHz, 64.6 μs), (7 kHz, 44.2 μs), (7kHz, 48.8 μs). A linear bounded region 100 c around points 102 is alsodefined for frequencies greater than or equal to 7 kHz and less than orequal to 10 kHz: (7 kHz, 44.2 μs), (7 kHz, 48.8 μs), (10 kHz, 29.9 μs),(10 kHz, 27.1 μs). Such regions 100 thus comprise information relatingfrequency and pulse width at which stimulation pulses are formed toprovide pain relief without paresthesia in the frequency range of 1 kHzto 10 kHz.

FIG. 11B provides an alternative analysis of the resulting relationshipbetween optimal pulse width and frequency. In this example, regions 100a-100 c are defined based upon the standard error (SE) calculated ateach frequency. Thus, points 102 defining the corners of the regions 100a-c are simply located at the extent of the SE error bars at eachfrequency (PW+SE, and PW−SE), even though these error bars are ofdifferent magnitudes at each frequency. Thus, astatistically-significant reduction in pain without paresthesia occursin or on the linearly bounded region 100 a of points (1 kHz, 96.3 μs),(1 kHz, 112 μs), (4 kHz, 73.8 μs), and (4 kHz, 62.2 μs). The linearbounded regions 100 b and 100 c are similar, and because the points 102defining them are set forth in chart at the top of FIG. 11B, they arenot repeated here.

FIG. 11C provides another analysis of the resulting relationship betweenoptimal pulse width and frequency. In this example, regions 100 a-100 care defined based upon the standard deviation (SD) calculated at eachfrequency, which is larger than the standard error (SE) metric used tothis point. Points 102 defining the corners of the regions 100 a-c arelocated at the extent of the SD error bars at each frequency (PW+SD, andPW−SD), although points 102 could also be set within the error bars,similar to what was illustrated earlier with respect to FIG. 11A. In anyevent, a statistically-significant reduction in pain without paresthesiaoccurs in or on the linearly bounded region 100 a of points (1 kHz, 69.6μs), (1 kHz, 138.4 μs), (4 kHz, 93.9 μs), and (4 kHz, 42.1 μs). Thelinear bounded regions 100 b and 100 c are similar, and because thepoints 102 defining them are set forth in chart at the top of FIG. 11C,they are not repeated here.

More generally, although not illustrated, regions within the frequencyrange of 1 kHz to 10 kHz where sub-perception efficacy was achievedcomprises linearly-bounded region 100 a (1 kHz, 50.0 μs), (1 kHz, 200.0μs), (4 kHz, 110.0 μs), and (4 kHz, 30.0 μs); and/or linearly-boundedregion 100 b (4 kHz, 110.0 μs), (4 kHz, 30.0 μs), (7 kHz, 30.0 μs), and(7 kHz, 60.0 μs); and/or linearly-bounded region 100 c (7 kHz, 30.0 μs),(7 kHz, 60.0 μs), (10 kHz, 40.0 μs), and (10 kHz, 20.0 μs).

In summary, one or more statistically-significant regions 100 can bedefined for the optimal pulse width and frequency data taken for thepatients in the study to arrive at combinations of pulse width andfrequency that reduce pain without the side effect of paresthesia withinthe frequency range of 1 kHz to 10 kHz, and different statisticalmeasures of error can be used to so define the one or more regions.

FIGS. 12A-12D show the results of testing other patients withsub-perception stimulation therapy at frequencies at or below 1 kHz.Testing of the patients generally occurred after supra-perception sweepspot searching occurred to select appropriate electrodes (E), polarities(P) and relative amplitudes (X %) for each patient (see FIGS. 7A-7D),although again the sub-perception electrodes used could vary from thoseused during the supra-perception sweet spot search (e.g., using MICC).Patients were tested with sub-perception stimulation using symmetricbiphasic bipoles, although the form of pulses used during sub-perceptiontherapy could vary.

FIG. 12A shows the relationship between frequency and pulse width atwhich effective sub-perception therapy was reported by patients forfrequencies of 1 kHz and below. Note that the same patient selection andtesting criteria described earlier (FIG. 9) can be used when evaluatingfrequencies at or below 1 kHz, with the frequencies adjusted asappropriate.

As can be seen, at each frequency tested, the optimal pulse width againfell within a range. For example, at 800 Hz, patients reported goodresults when the pulse width fell within a range of 105-175microseconds. The upper end of the pulse width range at each frequencyis denoted PW(high), while the lower end of the pulse width range ateach frequency is denoted PW(low). PW(middle) denotes the middle (e.g.,average) of the PW(high) and PW(low) at each frequency. At each of thetested frequencies the amplitude of the current provided (A) wastitrated down to sub-perception levels, such that the patient could notfeel paresthesia. Typically, the current was titrated to 80% of thethreshold at which paresthesia could be sensed. Because each patient'sanatomy is unique, the sub-perception amplitude A could vary frompatient to patient. The pulse width data depicted comprises the pulsewidth of only the first phase of the stimulation pulses.

Table 1 below expresses the optimal pulse width versus frequency data ofFIG. 12A in tabular form for frequencies at or below 1 kHz, with thepulse widths expressed in microseconds:

TABLE 1 Frequency PW (low) PW (middle) PW (high) (Hz) (μs) (μs) (μs)1000  90 120 150  800 105 140 175  600 120 160 200  400 140 183 225  200160 210 260  100 195 260 325  50 230 300 370  10 265 350 435

As with the analysis described earlier for frequencies in a range of 1kHz to 10 kHz (FIGS. 10A-11C), the data may be broken down to definedifferent regions 300 i at which effective sub-perception therapy isrealized below 1 kHz. For example, regions of effective sub-perceptiontherapy may be linearly bounded between various frequencies and the highand low pulse widths that define effectiveness. For example, at 10 Hz,PW(low)=265 microseconds and PW(high)=435 microseconds. At 50 Hz,PW(low)=230 microseconds and PW(high)=370 microseconds. Therefore, aregion 300 a that provides good sub-perception therapy is defined by thelinearly bounded region of points (10 Hz, 265 μs), (10 Hz, 435 μs), (50Hz, 370 μs), and (50 Hz, 230 μs). Table 2 defines the points thatlinearly bind each of the regions 300 a-300 g shown in FIG. 12A:

TABLE 2 region Bounded by points (Hz, μs) 300a (10, 265), (10, 435),(50, 370), (50, 230) 300b (50, 230), (50, 370), (100, 325), (100, 195)300c (100, 195), (100, 325), (200, 260), (200, 160) 300d (200, 160),(200, 260), (400, 225), (400, 140) 300e (400, 140), (400, 225), (600,200), (600, 120) 300f (600, 120), (600, 200), (800, 175), (800, 105)300g (800, 105), (800, 175), (1000, 150), (1000, 90)

Regions of sub-perception therapeutic effectiveness at frequencies at orbelow 1 kHz may be defined in other statistically-significant ways, suchas those described earlier for frequencies in the range of 1 kHz to 10kHz (FIGS. 11A-11C). For example, regions 300 i may be defined byreference to the pulse width at the middle of the ranges at eachfrequency, PW(middle). PW(middle) may comprise for example an averageoptimal pulse width reported by patients at each frequency, rather thanas a strict middle of an effective range reported by those patients.PW(high) and PW(low) may then be determined as a statistical variancefrom the average PW(middle) at each frequency, and used to set the upperand lower bounds of effective sub-perception regions. For example,PW(high) may comprise average PW(middle) plus a standard deviation orstandard error, or a multiples of such statistical measures; PW(low) maylikewise comprise average PW(middle) minus a standard deviation orstandard error, or a multiple of such statistical measures. PW(high) andPW(low) may also be determined from average PW(middle) in other ways.For example, PW(high) may comprise average PW(middle) plus a setpercentage, while PW(low) may comprise PW(middle) minus a setpercentage. In summary, one or more statistically-significant regions300 can be defined for the optimal pulse width and frequency data atfrequencies at or below 1 kHz that reduce pain using sub-perceptionstimulation without the side effect of paresthesia.

Also shown in FIG. 12A are average patient pain scores (NRS scores)reported by patients when optimal pulse widths are used for differentfrequencies at 1 kHz or below. Prior to receiving SCS therapy, patientsinitially reported pain scores with an average of 7.92. After SCSimplantation, and using the sub-perception stimulation at optimal pulsewidths with the ranges shown at each frequency, the patients' averagepain scores dropped significantly. At 1 kHz, 200 Hz, and 10 Hz, patientsreported average pain scores of 2.38, 2.17, and 3.20 respectively. Thusclinical significance with respect to pain relief is shown when theoptimal pulse widths are used at or below 1 kHz with sub-perceptiontherapy.

The optimal pulse width versus frequency data of FIG. 12A forfrequencies at or below 1 kHz is analyzed in FIG. 12B from theperspective of the middle pulse width, PW(middle) at each frequency (F).As shown, the relationships 310 a-310 d follows statisticallysignificant trends, as evidenced by the various regression models shownin FIG. 12B and summarized in Table 3 below:

TABLE 3 Correlation Regression coefficient model Relationship (PW(middle) in μs) R² Linear PW (middle) = −0.2F + 294.4 0.835 (310a)Polynomial PW (middle) = 0.0002F² −0.461F + 332.38 0.936 (310b) Power PW(middle) = 679.1 × ^(−0.23) 0.935 (310c) Logarithmic PW (middle) =−50.83 ln (F) + 482.8 0.982 (310d)

Other fitting methods could be used to establish other informationrelating frequency and pulse width at which stimulation pulses areformed to provide sub-perception pain relief without paresthesia.

Regression analysis can also be used to define statistically relevantregions such as 300 a-300 g where sub-perception therapy is effective ator below 1 kHz. For example, and although not shown in FIG. 12B,regression can be performed for PW(low) v. F to set a lower boundary ofrelevant regions 300 i, and regression can be performed for PW(high) v.F to set an upper boundary of relevant regions 300 i.

Note that the relationship between optimal pulse width and frequencydepicted in FIG. 12A is not simply an expected relationship betweenfrequency and duty cycle (DC), as FIG. 12C shows. As was the case whenthe 1 kHz to 10 kHz frequency range was tested (FIG. 11A), the dutycycle of the optimal pulse widths is not constant at 1 kHz and below.Again, there is significance to the optimal pulse widths beyond a merescaling of the frequency. Nonetheless, most of the pulse widths observedto be optimal at 1 kHz and below are greater than 100 microseconds. Suchpulse widths are not even possible at higher frequencies. For example,at 10 kHz, both pulse phases have to fit within a 100 us period, so PWlonger than 100 are not even possible.

FIG. 12D shows further benefits achieved in using sub-perception atfrequencies of 1 kHz and below, namely reduced power consumption. Twosets of data are graphed. The first data set comprises the averagecurrent drawn by the battery in the patients' IPG or ETS (AVG Ibat) ateach frequency using the optimal pulse width for that patient (FIG. 12A)and the current amplitude A necessary to achieve sub-perceptionstimulation for that patient (again, this amplitude can vary for each ofthe patients). At 1 kHz, this average battery current is about 1700microamps. However, as the frequency is reduced, this average batterycurrent drops, to about 200 microamps at 10 Hz. The second data setlooks at power consumption from a different vantage point, namely thenumber of days that an IPG or ETS with a fully-charged rechargeablebattery can operate before recharge is required (“discharge time”). Aswould be expected based on the average battery current data, thedischarge time is lower at higher frequencies when the average batterycurrent is higher (e.g., about 3.9 days at 1 kHz, depending on variouscharging parameters and settings), and is higher at lower frequencieswhen the average battery current is lower (e.g., about 34 days at 10 Hz,depending on various charging parameters and settings). This issignificant: not only can effective sub-perception therapy be providedat 1 kHz and below when optimal pulse widths are used; powerconsumptions is greatly lowered, which places less stress on the IPG orETS, and allows it to operate from longer periods of time. As notedabove, excessive power consumption is a significant problem whensub-perception therapy is traditionally used at higher frequencies. Notethat the data of FIG. 12D could also be analyzed in terms of meancharge-per-second (MSC), as described earlier for the 1 kHz to 10 kHzdata (FIG. 10B).

FIGS. 13A and 13B shows the results of additional testing that verifiesthe frequency versus pulse width relationships just presented. Here,data is shown for 25 patients tested using sub-perception stimulation atfrequencies of 10 kHz and below. FIG. 13A shows two different graphsshowing the result for frequencies of 10 k and below(lower graph) andfor frequencies of 1 kHz and below (upper graph). Mean values are shownfrequencies and pulse width values at which optimal sub-perceptiontherapy is produced. Upper and lower bands denote one standarddeviation's variance (+STD and −STD) above and below the mean. FIG. 13Bshows curve fitting results as determined using mean values. Data for 1kHz and below is fit with an exponential function and with a powerfunction, resulting in relationship PW=159e^(−0.01F)+220e^(−0.00057F)and PW=761−317 F^(0.10), both of which well fit to the data. Data for 10kHz and below is fit with a power function, yielding PW=−1861+2356F^(−0.024), again with a good fit. The data could bit fit to othermathematical functions as well.

Once determined, the information 350 relating frequency and pulse widthfor optimal sub-perception therapy without paresthesia can be stored inan external device used to program the IPG 10 or ETS 40, such as theclinician programmer 50 or external controller 45 described earlier.This is shown in FIG. 14, in which the control circuitry 70 or 48 of theclinician programmer or external controller is associated with regioninformation 100 i or relationship information 98 i for frequencies inthe 1 kHz to 10 kHz range, and region information 300 i or relationshipinformation 310 i for frequencies at or below 1 kHz. Such informationcan be stored in memory within or associated with the control circuitry.Storing of this information with the external device is useful toassisting the clinician with sub-perception optimization, as describedfurther below. Alternatively, and although not shown, the informationrelating frequency and pulse width can be stored in the IPG 10 or ETS40, thus allowing the IPG or ETS to optimize itself without clinician orpatient input.

Information 350 can be incorporated into a fitting module. For example,fitting module 350 could operate as a software module within clinicianprogrammer software 66, and may perhaps be implemented as an optionselectable within the advanced 88 or mode 90 menu options selectable inthe clinician programmer GUI 64 (FIG. 6). Fitting module 350 could alsooperate in the control circuitry of the IPG 10 or ETS 40.

The fitting module 350 can be used to optimize pulse width whenfrequency is known, or vice versa. As shown at the top of FIG. 14, theclinician or patient can enter a frequency F into the clinicianprogrammer 50 or external controller 45. This frequency F is passed tothe fitting module 350 to determine a pulse width PW for the patient,which is statistically likely to provide suitable pain relief withoutparesthesia. Frequency F could for example be input to the relationships98 i or 310 i to determine the pulse width PW. Or, the frequency couldbe compared to the relevant region 100 i or 300 i within which thefrequency falls. Once the correct region 100 i or 300 i is determined, Fcan be compared to the data in regions to determine a pulse width PW,which may perhaps be a pulse width between the PW+X and PW−X boundariesat the given frequency, as described earlier. Other stimulationparameters, such as amplitude A, active electrodes E, their relativepercentage X %, and electrode polarity P can be determined in othermanners, such as those described below, to arrive at a completestimulation program (SP) for the patient. Based on the data from FIG.10B, an amplitude near 3.0 mA might be a logical starting point, as thisamplitude was show to be preferred by patients in the 1 kHz to 10 kHzrange. However, other initial starting amplitudes may be chosen as well,which amplitudes for sub-perception therapy may be dependent onfrequency. The bottom of FIG. 14 shows use of the fitting module 350 inreverse—that is to pick a frequency given a pulse width. Note that inthe algorithms that follow or even when used outside of any algorithm,in one example, the system can allow the user to associate the frequencyand pulse width such that when the frequency or pulse width is changed,the other of the pulse width or frequency is automatically changed tocorrespond to an optimal setting. In some embodiments, associating thefrequency and pulse width in this manner can comprise a selectablefeature (e.g., in GUI 64) useable when sub-perception programming isdesired, and associating the frequency and pulse width can be unselectedor unselectable for use with other stimulation modes.

FIG. 15 shows an algorithm 355 that can be used to providesub-perception therapy to an SCS patient at frequencies of 10 kHz orlower, and summarizes some of the steps already discussed above. Steps320-328 describe the supra-perception sweep spot search. A user (e.g.,clinician) selects electrodes to create a bipole for the patient (320),for example, by using the GUI of the clinician programmer. This bipoleis preferably a symmetric biphasic bipole and may comprise a virtualbipole, as described earlier.

This bipole is telemetered along with other simulation parameters to theIPG or ETS for execution (321). Such other stimulation parameters canalso be selected in the clinician programmer using the GUI. As adefault, the frequency F can equal 90 Hz and the pulse width (PW) canequal 200 microseconds, although this is not strictly necessary andthese values can be modified. At this point, if the bipole provided bythe IPG or ETS is not supra-perception, i.e., if paresthesia is not feltby the patient, the amplitude A or other stimulation parameters can beadjusted to make it so (322). The bipole's effectiveness is then gaugedby the patient (324) to see how well the bipole is covering thepatient's pain site. NRS or other score rating systems can be used tojudge effectiveness.

If the bipole is not effective, or if it is still desired to search, anew bipole can be tried (326). That is new electrodes can be selectedpreferably in manner which moves the bipole to a new location, along apath 296 as described earlier with reference to FIGS. 7A-7D. This newbipole can then again be telemetered to the IPG or ETS (321) andadjustments made if necessary to render the bipole supra-perceptive(322). If the bipole is effective, or if the searching is done and amost effective bipole has been located, that bipole may optionally bemodified (328) prior to sub-perception therapy. Such modification asdescribed above can involve selecting other electrodes proximate to theselected bipole's electrodes to modify the field shape in the tissue toperhaps better cover the patient's pain. As such, the modification ofstep 328 may change the bipole used during the search to a virtualbipole, or a tripole, etc.

Modification of other stimulation parameters can also occur at thispoint. For example, the frequency and pulse width can also be modified.In one example, a working pulse width can be chosen which provides good,comfortable paresthesia coverage (>80%). This can occur by using afrequency of 200 Hz for example, and starting with a pulse width of 120microseconds for example. The pulse width can be increased at thisfrequency until good paresthesia coverage is noted. An amplitude in therange of 4 to 9 mA may be used for example.

At this point, the electrodes chosen for stimulation (E), theirpolarities (P), and the fraction of current they will receive (X %) (andpossible a working pulse width) are known and will be used to providesub-perception therapy. To ensure that sub-perception therapy isprovided, the amplitude A of the stimulation is titrated downward to asub-perception, paresthesia free level (330), and telemetered to the IPGor ETS. As described above, the amplitude A may be set below anamplitude threshold (e.g., 80% of the threshold) at which the patientcan just start to feel paresthesia.

At this point, it can be useful to optimize the frequency and pulsewidth of the sub-perception therapy that is being provided to thepatient (332). While the frequency (F) and pulse width (PW) used duringsweet spot searching can be used for sub-perception therapy, benefit ishad by additionally adjusting these parameters to optimal values inaccordance with the regions 100 i or relationships 98 i established atfrequencies in the 1 kHz to 10 kHz range, or the regions 300 i orrelationships 310 i established at frequencies at or below 1 kHz. Suchoptimization may use the fitting module 350 of FIG. 14, and can occur indifferent ways, and a few means of optimization 332 a-332 c are shown inFIG. 15. Option 332 a for instance allows the software in either theclinician programmer or the IPG or ETS to automatically select both afrequency (≤10 kHz) and pulse width using the region or relationshipdata correlating frequency to pulse width. Option 332 a might use theworking pulse width determined earlier (328), and choose a frequencyusing the regions or relationships. Option 332 b by contrast allows theuser (clinician) to specify (using the GUI of the clinician program)either the frequency (≤10 kHz) or the pulse width. The software can thenselect an appropriate value for the other parameter (pulse width orfrequency (≤10 kHz), again using regions or the relationships. Again,this option might use the working pulse width determined earlier toselect an appropriate frequency. Option 332 c allows the user to enterboth the frequency (≤10 kHz) and the pulse width PW, but in a mannerthat is constrained by the regions or the relationships. Again, thisoption may allow the use to enter the working pulse width and afrequency that is appropriate for that working frequency, depending onthe regions or relationships. The GUI 64 of the clinician programmermight in this example not accept inputs for F and PW that do not fallwithin the regions or along the relationships because such values wouldnot provide optimal sub-perception therapy.

Frequency or pulse width optimization can occur other ways that moreeffectively search the desired portion of the parameter space. Forexample, a gradient descent, binary search, simplex method, geneticalgorithm, etc. can be used for the search. A machine learning algorithmthat has trained using data from patients could be considered.

Preferably, when optimizing the frequency (≤10 kHz) and pulse width atstep 332, these parameters are selected in a manner that reduces powerconsumption. In this regard, it is preferable that the lowest frequencybe chosen, as this will reduce mean charge per second (MCS), reduce theaverage current drawn from the battery in the IPG or ETS, and thusincrease the discharge time, as discussed earlier with respect to FIGS.10B and 12D. Lowering the pulse width if possible will also reducebattery draw and increase the discharge time.

At this point all relevant stimulation parameters (E, P, X, I, PW, and F(≤10 kHz)) are determined and can be sent from the clinician programmerto the IPG or ETS for execution (334) to provide sub-perceptionstimulation therapy for the patient. It is possible that adjustment ofthe optimal pulse width and frequency (≤10 kHz) (332) may cause thesestimulation parameters to provide paresthesia. Therefore, the amplitudeof the current A can once again be titrated downward to sub-perceptionlevels if necessary (336). If necessary, the prescribed sub-perceptiontherapy can be allowed a period of time to wash in (338), although asmentioned earlier this may not be necessary as the supra-perceptionsweet spot search (320-328) has selected electrodes for situation thatwell recruit the patient's pain site.

If sub-perception therapy is not effective, or could use adjustment, thealgorithm can return to step 332 to selection of a new frequency (≤10kHz) and/or pulse width in accordance with the regions or relationshipsdefined earlier.

It should be noted that not all parts of steps of the algorithm of FIG.15 need be performed in an actual implementation. For example, ifeffective electrodes are already known (i.e., E, P, X), then thealgorithm may begin with sub-perception optimization using theinformation relating frequency and pulse width.

FIG. 16 shows another manner in which fitting module 350 (FIG. 14) canbe used to determine optimal sub-perception stimulation for a patient atfrequencies of 10 kHz or less. In FIG. 16, the fitting module 350 isagain incorporated within or used by an algorithm 105, which again canbe executed on the external device's control circuitry as part of itssoftware, or in the IPG 10. In the algorithm 105, the fitting module 350is used to pick initial pulse widths given a particular frequency.Algorithm 105 is however more comprehensive as it will test and optimizeamplitudes and further optimize pulse widths at different frequencies.As explained further below, algorithm 105 further optionally assists inpicking optimized stimulation parameters that will result in the lowestpower requirements that are most considerate of the IPG's battery 14.Some steps illustrated in FIG. 16 for algorithm 105 are optional, andother steps could be added as well. It is assumed that a sweet spotsearch for a patient being tested by algorithm 105 has already occurred,and that electrodes (E, P, X) have already been chosen and preferablywill remain constant throughout operation of the algorithm. However,this is not strictly required, as these electrode parameters can also bemodified, as described above.

Algorithm 105 begins by picking an initial frequency (e.g., F1) withinthe range of interest (e.g., ≤10 kHz). Algorithm 105 then passes thisfrequency to the fitting module 350, which uses the relationships and/orregions determined earlier to pick an initial pulse width PW1. Forsimplicity, fitting module 350 is illustrated in FIG. 16 as a simplelook up table of pulse width versus frequency, which can compriseanother form of information relating frequency and pulse width at whichstimulation pulses are formed to provide pain relief withoutparesthesia. Selection of a pulse width using fitting module 350 couldbe more sophisticated, as described earlier.

After selection of a pulse width for the given frequency, stimulationamplitude A is optimized (120). Here, a number of amplitudes are chosenand applied to the patient. In this example, the chosen amplitudes arepreferably determined using an optimal amplitude A determined at eachfrequency (see, e.g., FIG. 10B). Thus, amplitudes at A=A2, below (A1),and above (A3) are tried by the patient for a period (e.g., two dayseach). A best of these are picked by the patient. At this point, furtheradjustments to amplitude can be tried to try and hone in on an optimalamplitude for the patient. For example, if A2 is preferred, amplitudesslightly above (A2+Δ) and below (A2−Δ) below this can be tried for aperiod. If a lower value of A1 was preferred, an even lower amplitude(A1−Δ) can be tried. If a higher value of A3 was preferred, an evenhigher amplitude (A3+Δ) can be tried. Ultimately, such iterative testingof amplitude arrives at an effective amplitude for the patient that doesnot induce paresthesia.

Next, the pulse width can be optimized for the patient (130). As withamplitude, this can occur by slightly lowering or increasing the pulsewidth chosen earlier (350). For example, at a frequency of F1 and aninitial pulse width of PW1, the pulse width may be lowered (PW1−Δ) andincreased (PW1+Δ) to see if such settings are preferred by the patient.Further iterative adjustment of amplitude and pulse width may occur atthis point, although this is not illustrated.

In short, at a given frequency, an initial pulse width (350) (andpreferably also an initial amplitude (120)) are chosen for a patient,because it would be expected that these values would likely provideeffective and paresthesia-free pain relief. Nonetheless, because eachpatient is different, the amplitude (120) and pulse width (130) are alsoadjusted from the initial values for each patient.

Thereafter, the optimal stimulation parameters determined for thepatient at the frequency being tested are stored in the software (135).Optionally, a mean charge per second (MCS) indicative of the neural dosethe patient receives, or other information indicative of power draw(e.g., average Ibat, discharge time) is also calculated and also stored.If still further frequencies in the range of interest have not beentested (e.g., F2), they are then tested as just described.

Once one or more frequencies have been tested, stimulation parameterscan be chosen for the patient (140), using the optimal stimulationparameters stored earlier for the patient at each frequency (135).Because the stimulation parameters at each frequency are suitable forthe patient, the stimulation parameters chosen can comprise that whichresults in the lowest power draw (e.g., the lowest) MSC. This isdesired, because these stimulation parameters will be easiest on theIPG's battery. It might be expected that the stimulation parametersdetermined by algorithm 105 to have the lowest MCS would comprise thosetaken at the lowest frequency. However, every patient is different, andtherefore this might not be the case. Once the stimulation parametershave been chosen, further amplitude optimization can be undertaken(150), with the goal of choosing a minimum amplitude that providessub-perception pain relief without paresthesia.

The results of further investigations are shown in FIGS. 17-22D, withthe goal of providing optimal sub-perception modelling that takes intoaccount perception threshold (pth) as well and frequency (F) and pulsewidth (PW). Perception threshold can be a significant factor to considerwhen modelling sub-perception stimulation, and using such modelinginformation to determine optimal sub-threshold stimulation parametersfor each patient. Perception threshold, pth, comprises a lowestmagnitude at which the patient can feel the effects of paresthesia(e.g., in mA), with magnitudes below this causing sub-perceptionstimulation. It is a reality that different patients will have differentperception thresholds. Different perception thresholds result insignificant part because the electrode array in some patients may becloser to spinal neural fibers than in other patients. Such patientswill thus experience perception at lower magnitudes, i.e., pth will belower for such patients. If the electrode array in other patients isfarther from spinal neural fibers, the perception threshold pth will behigher. Improved modelling takes an understanding of pth into account,because the inclusion of this parameter can be used to suggest anoptimal amplitude A for a patient's sub-perception stimulation inadditional to optimal frequencies and pulse widths.

With this in mind, data was taken from patients to determine not onlywhich frequencies and pulse widths they found optimal as describedearlier, but also to determine the perception threshold at thosefrequencies and pulse widths. The resulting model 390 in shown in FIG.17. This model 390 was determined based on testing with a sample ofpatients (N=25), with FIG. 17 showing mean values as determined bythree-dimensional regression fitting, which yields model 390 as asurface in Frequency-Pulse Width-Perception Threshold space. Data asrepresented in FIG. 17 was taken at frequencies of 1 kHz and below. Dataat these frequencies is of particular interest, because, as alreadymentioned, lower frequencies are more considerate of energy usage in anIPG or ETS, and hence is it particularly desirable to prove the utilityof sub-perception stimulation in this frequency range. As can be seen bythe equation in FIG. 17, data taken from the patient was modelled with agood fit by assuming that frequency varies with both pulse width(a(PW)b) and perception threshold pth (c(pth)d) in accordance with powerfunctions. While these functions provided suitable fitting, other typesof mathematical equations could be used for fitting as well. Model 390as surface fit yields the following: F(PW,pth)=4.94×10⁸ (PW)−2.749+1.358(pth)². Note that frequency, pulse width, and perception threshold arenot simply proportionally related or inversely proportionately relatedmodel 390, but are instead related by non-linear functions.

FIG. 18A shows further observations noticed from tested patients, andprovides another modelling aspect that along with model 390 can be usedto determine optimal sub-threshold stimulation parameters for a patient.FIG. 18A shows how perception threshold pth varies as a function ofpulse width for the tested patients, with each patient being representedby a different line in the graph of FIG. 18A. Analysis of each of thelines suggests that the relationship between pth and PW can be wellmodeled with a power function, i.e., pth(PW)=i(PW)j+k, although againother mathematic functions could be used for fitting as well. The dataof FIG. 18A was taken for each patient at a nominal frequency such as200 to 500 Hz, with further analysis confirming that the results do notvary considerably with frequency (at least at frequencies of 150 Hz andhigher, using biphasic pulses with active recharge). Pulse widths inFIG. 18A were limited to the range of approximately 100 to 400microseconds. Limiting analysis to these pulse widths is reasonable,because previous testing (e.g., FIG. 12A) shows pulse widths in thisrange to have unique sub-perception therapeutic effectiveness atfrequencies of 1 kHz and lower. FIG. 18B shows another example of pthversus pulse width for different patients, and shows another equationthat can be used to model the data. Specifically, a Weiss-Lapicque, orstrength-duration, equation is used in this example, which relates theamplitude and pulse width required to attain a threshold. The equationtakes the form pth=(1/a)(1+b/PW), and when data for different patientsare averaged, constants a=0.60 and b=317 result with good fittingresults, where these values represent the mean constant parametersextracted from the population data.

FIG. 19 shows still further observations noted from the tested patients,and provides yet another modeling aspect. FIG. 19 in effect shows howoptimal sub-perception amplitudes A for patients vary in accordance witha patients' perception thresholds pth, as well as pulse width. In thegraph in FIG. 19, the vertical axis plots a parameter Z, which relates apatients' perception thresholds pth and their optimal amplitudes A(which in a sub-perception therapy would be lower than pth).Specifically, Z is the optimal amplitude expressed as a percentage ofpth, i.e., Z=A/pth. As FIG. 19 shows, Z varies with pulse width. Atsmaller pulse widths (e.g., 150 microsecond), Z is relatively low,meaning that the optimal amplitude A for patients was noted to beconsiderably lower than their perception thresholds (e.g., A=40% ofpth). At longer pulse widths (e.g., 350 microseconds), Z is higher,meaning that the optimal amplitude A for patients was noted to be closerto their perception thresholds (e.g., A=70% of pth). Z and PW as notedfrom testing various patients generally have a linear relationship overthe pulse widths tested, and so linear regression was used to determinetheir relationship, yielding Z=0.0017 (PW)+0.1524 (395). Again, testingin FIG. 19 was limited to the general range of 100 to 400 microsecondsnoted to be useful for sub-perception therapy at less than 1 kHz. Itmight be expected that testing over a wider pulse width range (e.g.,less than 100 microseconds, or greater than 400 microseconds) would showsome variance from the linear relationship noted. For example, Z mightlevel off to some value smaller than 1 for pulse widths higher than 400microseconds, and might level off to a value greater than 0 for pulsewidths shorter than 100 microseconds. Because Z varies with pulse widthas curve fit, and because Z also varies with optimal amplitude A andperception threshold pth (Z=A/pth), the modelling of FIG. 19 allowsoptimal amplitude A to be modelled as a function of both perceptionthreshold pth and pulse width PW, i.e., A=pth [0.0017 (PW)+0.1524](396). The inventors observe that optimal amplitude A is generallyinvariant to changes in frequency and pulse width. However, perceptionthreshold varies with pulse width. Thus, Z varies with pulse width,while optimal amplitude A may not.

Recognizing and modeling these observations, the inventors havedeveloped an algorithm 400 that can be used to provide personalizedsub-perception therapy for particular patients. This algorithm 400 canlargely be implemented on the clinician programmer 50, and results inthe determination of a range of optimal sub-perception parameters (e.g.,F, PW, and A) for the patient. Preferably, as last step in the algorithm400, the range or volume of optimal sub-perception parameters istransmitted to the patient's external controller 45 to allow the patientto adjust their sub-perception therapy within this range or volume.

The algorithm 400, shown starting in FIG. 20A, starts in step 402 bydetermining for a given patient the sweet spot in the electrode array atwhich therapy should be applied—i.e., by identifying which electrodesshould be active and with what polarities and percentages (X %). Theresults of sweet spot searching may already be known for a givenpatient, and thus step 402 should be understood as optional. Step 402and subsequent steps may be accomplished using clinician programmer 50.

At step 404, a new patient is tested by providing situation pulses, andin the algorithm 400, such testing involves measuring the patient'sperception threshold pth at various pulse widths during a testingprocedure, using the sweet spot electrodes already identified at step402. As discussed earlier with respect to FIGS. 18A and 18B, testing ofdifferent pulse widths can occur at a nominal frequency such as in therange of 200 to 500 Hz. Determining pth at each given pulse widthinvolves applying the pulse width, and gradually increasing theamplitude A to a point where the patient reports feeling the stimulation(paresthesia), resulting in a pth expressed in terms of amplitude (e.g.,milliamps). Alternatively, determining pth at each given pulse width caninvolve decreasing the amplitude A to a point where the patient reportsno longer feels the stimulation (sub-threshold). Testing 404 of aparticular patient is shown graphically and in tabular form in FIG. 20A.Here, it is assumed that the patient in question has a paresthesiathreshold pth of 10.2 mA at a pulse width of 120 microseconds; a pth of5.9 mA at a pulse width of 350 microseconds, and other values betweenthese.

Next, in step 406, the algorithm 400 in the clinician programmer 50models the pth v. PW data points measured in step 404, and curve fitsthem to a mathematical function. This mathematical function could be onenoticed earlier to well model pth and PW in other patients, such as apower function pth(PW)=i(PW)^(j)+k or the Weiss Lapicque equation, asdiscussed earlier with respect to FIGS. 18A and 18B. However, any othermathematical function could be used to curve fit the current patient'sdata measurements, such as a polynomial function, and exponentialfunction, etc. In the illustrated data, a power function well models thedata, yielding pth(PW)=116.5×PW^(−0.509). (For simplicity, constant ‘k’has been ignored). The measured data in table 404, as well as thedetermined curve-fit relationship pth(PW) 406 determined for the patientmay be stored in memory in the clinician programmer 50 for use insubsequent steps.

Next, and referring to FIG. 20B, algorithm 400 proceeds to compare thepth(PW) relationship determined in step 406 to the model 390. This isexplained with reference to a table shown in FIG. 20B. In this table,values for pth and PW are populated, as determined by the pth(PW) (406)determined in FIG. 20A. As can be seen, discrete pulse width values ofinterest (100 microseconds, 150 microseconds, etc.) may be used (withmay vary from the exact pulse widths used during patient testing in step404). While only six rows of PW v. pth values are shown in the table ofFIG. 20B, this could be a much longer vector of values, with pthdetermined at discrete PW steps (such as 10 microsecond steps).

The pth v. PW values (from function 406) are in step 408 comparedagainst the three-dimensional model 390 to determine frequencies F thatwould be optimal at these various pth v. PW pairs. In other words, thepth and PW values are provided as variables into the surface fitequation (F(PW,pth)) 390 in FIG. 17 to determine optimal frequencies,which frequencies are also shown as populated into the chart in FIG.20B. At this point, the table in FIG. 20B represents a vector 410relating pulse widths and frequencies that are optimal for the patient,and that in addition include the perception threshold for the patient atthese pulse width and frequencies values. In other words, a vector 410represents values within the model 390 that are optimal for the patient.Note that vector 410 for the patient can be represented as a curved linealong the three-dimensional model 390, as shown in FIG. 20B.

Next, and as shown in step 412 in FIG. 20C, the vector 410 canoptionally be used to form another vector 413, which contains values ofinterest or more practically values that may be supportable by the IPGor ETS. For example, notice that vector 410 for the patient includesfrequencies at higher values (e.g., 1719 Hz), or otherwise at odd values(such as 627 and 197 Hz). Frequencies at higher values may not bedesirable to use, because even if effective for the patient, suchfrequencies will involve excessive power draws. See, e.g., FIG. 12D.Moreover, the IPG or ETS at issue may only be able to provide pulseswith frequencies at discrete intervals (such as in 10 Hz increments).Therefore, in vector 413, frequencies of interest or that are supportedare chosen (e.g., 1000 Hz, 400 Hz, 200 Hz, 100 Hz, etc.), and thencorresponding values for PW and pth are interpolated using vector 410.Although not shown, it may be useful to formulate vector 410 as anequation F(PW,pth)) to make vector 413 easier to populate. Nonetheless,vector 413 includes essentially the same information as vector 410,albeit at desirable frequencies. Realize that only certain pulse widthsmay be supportable by the IPG or ETS (e.g., in 10 microsecondincrements). Therefore, the pulse widths in vector 413 may be adjusted(e.g., rounded) to nearest supported values, although this isn't shownin the drawings.

Next, and referring to FIG. 20D, the algorithm 400 in step 414determines optimal amplitudes for the pulse width and pth values invector 413 (or vector 410 if vector 413 isn't used). This occurs byusing the amplitude function 396 determined earlier in FIG. 19, i.e.,A(pth,PW). Using this function, an optimal amplitude A can be determinedfor each pth, PW pair in the table.

At this point, in step 416, optimal sub-threshold stimulation parametersF, PW, A 420 are determined as a model specific to the patient. Optimalstimulation parameters 420 may not need to include the perceptionthreshold, pth: although pth was useful to determine optimalsubthreshold amplitude A for the patient (step 414), it may no longer bea parameter of interest as it is not a parameter that the IPG or ETSproduces. However, in other examples discussed later, it can be usefulto include pth with the optimal parameters 420, as this can allow apatient to adjust their stimulation to a supra-perception level ifdesired. At this point, optimal stimulation parameters 420 may then betransmitted to the IPG or ETS for execution, or as shown in step 422,they may be transmitted to the patient's external controller 45, asdescribed next.

FIGS. 20E and 20F depict optimal parameters 420 in graphical form. Whileoptimal parameters 420 in this example comprise a three-dimensionalrange or line of coordinates (F, PW, and A), they are depicted in twotwo-dimensional graphs for easier illustration: FIG. 20E shows therelationship between frequency and pulse width, and FIG. 20F shows therelationship between frequency and amplitude. Note also that FIG. 20Fshows the paresthesia threshold pth, and additionally shows on theX-axis the pulse width corresponding to the various frequencies fromFIG. 20E. Note that the shapes of the data on these graphs could varyfrom patient to patient (e.g., based on the pth measurements of FIG.20A), and could also change depending on the underlying modelling used(e.g., FIGS. 17-19). The various shapes of the trends shown thus shouldnot be construed as limiting.

The optimal stimulation parameters 420 determined by the algorithm 400comprise a range or vector of values, comprising frequency/pulsewidth/amplitude coordinates that based on modeling (FIG. 17-19), and ontesting of the patient (step 404, FIG. 20A), will result insub-threshold stimulation that is optimal for that patient. Whileoptimal parameters 420 are shown for simplicity in tabular form in FIG.21, it should be understood these optimal parameters (0) may be curvefit using an equation that includes frequency, pulse, and amplitude(i.e., 0=f(F,PW,A)). Because each of these coordinates are optimal, itmay be reasonable to allow the patient to use them with their IPG orETS, and as a result the optimal parameters 420 may be sent from theclinician programmer 50 to the patient external controller 45 (FIG. 4)to allow the patient to select between them. In this regard, the optimalparameters 420, whether in tabular form or in the form of an equation,can be loaded into control circuitry 48 of the external controller 45.

Once loaded, the patient can access a menu in the external controller 45to adjust the therapy the IPG or ETS provides consistent with theseoptimal parameters 420. For example, FIG. 21 shows a graphical userinterface (GUI) of the external controller 45 as displayed on its screen46. The GUI includes means to allow the patient to simultaneously adjustthe stimulation within the range of determined optimal stimulationparameters 420. In one example, a slider is included in the GUI with acursor 430. The patient may select the cursor 430 and in this examplemove it to the left or right to adjust the frequency of stimulationpulses in their IPG or ETS. Moving it to the left reduces the frequencydown to a minimum value included in the optimal parameters 420 (e.g., 50Hz). Moving the cursor 430 to the right increases the frequency to amaximum values included in the optimal parameters (e.g., 1000 Hz). Asthe cursor 430 is moved and the frequency of stimulation is changed, thepulse widths and amplitudes are simultaneously adjusted as reflected inoptimal parameters 420. For example, at F=50 Hz, the amplitude isautomatically set to A=4.2 mA, and the pulse width is set to 413microseconds. At F=1000 Hz, the amplitude is set to A=3.7 mA, and thepulse width is set to 132 microseconds. In effect the cursor 430 allowsthe patient to navigate through the optimal parameters 420 to find aF/PW/A setting they prefer, or simply to choose stimulation parametersthat are still effective but require lower power draws from the IPG orETS (e.g., at lower frequencies). Note that the frequency, pulse width,and amplitude may not be adjusted proportionately or inverselyproportional with respect to each other but will follow non-linearrelationships in accordance with the underlying modelling.

In another example, it may be useful to allow the patient to adjuststimulation without knowledge of the stimulation parameters, i.e.,without displaying the parameters, which may be too technical for thepatient to understand. In this regard, the slider can be labeled with amore generic parameter, such as φ, which the patient can adjust, such asbetween 0 and 100%. The three-dimensional simulation parameters A, PW,and F can be mapped to this one-dimensional parameter φ (e.g., 4.2 mA,413 μs, and 50 Hz can equal 0% as shown). Generally speaking, thepatient may understand parameter φ as a sort of “intensity” or “neuraldose” which is higher and higher percentages. This may in fact be truegiven depending on the manner in which the optimal stimulationparameters 420 are mapped to  .

It should be appreciated that while the GUI of the external controller45 does allow the patient some flexibility to modify stimulationparameters for his IPG or ETS, it is also simple, and beneficiallyallows the patient to adjust all three stimulation parameterssimultaneously using a single user interface element, all while beingensured that the resulting stimulation parameters will provide optimalsub-threshold stimulation.

Other stimulation adjustment controls may be provided by the externalcontroller 45 as well. For example, as shown in FIG. 21, another slidercan allow the patient to adjust the duty cycle to control the extent towhich pulses will be continually running (100%) or completely off (0%).A duty cycle in the middle (e.g., 50%) will mean that pulses will runfor a period of time (from second to minutes) and will then be off forthat exact same duration. Because “duty cycle” may be a technicalconcept that a patient would not intuitively understand, note that theduty cycle may be labeled in a more intuitive manner. Thus, and asshown, the duty cycle adjustment may be labeled differently. Forexample, because lower duty cycles would affect lower power draws, theduty cycle slider may be label as a “battery saving” feature, as a“total energy” feature, a “total neural charge dose” feature, or thelike, which may be easier for the patient to understand. Duty cyclingmay also comprise a feature in the external controller 45 that is lockedto the patient, and only made accessible to a clinician for example,upon entering an appropriate password or other credentials. Note thatthe duty cycle could be smoothly adjusted, or made adjustable in pre-setlogical increments, such as 0%, 10%, 20%, etc. Duty cycle adjustment isnot show in subsequent user interface examples for simplicity, but couldbe used in such examples as well.

FIGS. 22A-22D address the practicality that the modeling leading to thedetermination of optimal parameters 420 may not be perfect. For example,model 390—modeling frequency as a function of PW and pth (F(PW,pth);FIG. 17)—is averaged from various patients, and can have somestatistical variance. This is illustrated simply in FIG. 22A by showingsurfaces 390+ and 390− that are higher and lower from the mean asreflected in surface model 390. Surfaces 390+ and 390− may representsome degree of statistical variance or error measure, such as plus orminus one sigma, and may in effect generically comprise error barsbeyond which the model 390 is no longer reliable. These error bars 390+and 390− (which may not be constant over the entire surface 390) canalso be determined from an understanding of the statistical variance inthe various constants assumed during modeling. For example, values a, b,c, and d in model 390 may be determined with different measures ofconfidence. As shown in FIG. 17, constants a, b, and c vary within a 95%confidence interval. For example, constant ‘a’ can range from 5.53×10⁷to 9.32×10⁸, as shown on the graph. (Here, it is assumed that constant dis simply 2 and does not vary). Likewise, values used to model therelationship of pth and pulse width (FIGS. 18A and 18B) may havedifferent measure of confidence, as may values m and n used to model therelationship between optional amplitude, pth, and PW (FIG. 19). Overtime, as data is taken on more patients, it would be expected that theconfidence of these models would improve. In this regard, note thatalgorithm 400 can easily be updated with new modeling information fromtime to time by loading new modeling information into the clinicianprogrammer 50.

Statistical variance means that optimal stimulation parameters may notcomprise discrete values, but may instead fall within a volume. This isillustrated in FIG. 22A as concerns the vector 410 determined for thepatient (see FIG. 20B). Given statistical variance, vector 410 maycomprise a rigid line within a volume 410′. In other words, there maynot be a one-to-one correspondence of PW, pth, and F, as was the casefor vector 410 in FIG. 20B. Instead, for any given variable (such aspulse width), the pth as determined for the patient (using the pth(PW)model in step 406) may vary in a range between statistically-significantmaximum and minimum values, as shown in FIG. 22B. Statistical variationin model 390 (FIG. 17) may also mean that maximum and minimumfrequencies may be determined for each maximum and minimum pth in step408. As this trickles through the algorithm 400, the optimal stimulationparameters 420 may also not have one-to-one correspondence betweenfrequency, pulse width, and amplitude. Instead, and as shown in FIG.22B, for any frequency, there may be a range of maximum and minimumoptimal pulse width of statistical significance, and a likewise a rangeof optimal amplitudes A. Effectively, then, optimal stimulationparameters 420′ may define a statistically-significant volume ofcoordinates in Frequency-Pulse Width-Amplitude space rather than a lineof coordinates. Paresthesia threshold pth may also vary within a range,and as noted earlier can be useful to include in the optimal stimulationparameters 420′, because pth may be helpful to permitting the patient tovary stimulation from sub-perception to supra-perception, as discussedin some later examples.

FIGS. 22C and 22D depict optimal parameters 420′ in graphical form,showing at each frequency a statistically-relevant range of pulsewidths, and a statistically-relevant range of amplitudes appropriate forthe patient. While optimal parameters 420′ in this example comprise athree-dimensional volume of coordinates (F, PW, and A), they aredepicted in two two-dimensional graphs for easier illustration, similarto what occurred earlier in FIGS. 20E and 20F: FIG. 22C shows therelationship between frequency and pulse width, and FIG. 22D shows therelationship between frequency and amplitude. Note also that FIG. 22Dshows the paresthesia threshold pth, which like pulse width andamplitude can statistically vary within a range. Optimal stimulationparameters 420 (determined without statistical variance, see FIGS. 20Eand 20F) are also shown for each of the parameters, and as expected fallwithin the broader volume for the parameters specified by 420′.

With a volume of optimal parameters 420′ defined, it may then be usefulto allow the patient to use his external controller 45 to navigatedifferent setting within this volume of optimal parameters 420′. This isshown in one example in FIG. 22E. Here, the GUI of the externalcontroller 45 displays not a single linear slider, but athree-dimensional volume representative of the volume 420′ of optimalparameters, with different axes representing changes the patient canmake in frequency, pulse width, and amplitude. As before, the GUI of theexternal controller 45 allows the patient some flexibility to modifystimulation parameters for his IPG or ETS, and allows the patient toadjust all three stimulation parameters simultaneously through oneadjustment action and using a single user interface element.

Different GUIs to allow the patient to navigate through the determinedvolume of optimal parameters 420′ are possible, and FIG. 22F showsanother example. In FIG. 22E, two sliders are shown. The first, a linearslider controlled by cursor 430 a, allows the patient to adjust thefrequency in accordance with frequencies reflected in the optimal volume420′. A second two-dimensional slider controlled by cursor 430 b allowsthe patient to adjust pulse width and amplitude at that frequency.Preferably, the range of pulse widths and amplitudes is constrained bythe optimal parameters 420′ and by the frequency already selected usingcursor 430 a. For example, if the user selected to use frequency F=400Hz, the external controller 45 can consult optimal parameters 420′ toautomatically determine an optimal range of pulse widths (e.g., 175 to210 microseconds) and amplitudes (3.7 to 4.1 mA) for the patient to useat that frequency. When the patient changes the frequency using cursor430 a, the range of permissible pulse widths and amplitudes selectableusing cursor 430 b can automatically change to ensure that sub-thresholdstimulation remains within the volume 420′ determined to bestatistically useful for the patient.

FIG. 23 shows another example in which a user can program settings forhis IPG 10 (or ETS) using the derived optimal stimulation parameters.Subsequent examples for completeness use determined volumes 420′ ofoptimal stimulation parameters, but vectors or ranges 420 of optimalstimulation parameters could be used as well.

FIG. 23 shows a user interface on screen 46 of the patient's externalcontroller 45, which allows the patent to select from a number ofstimulation modes. Such stimulation modes can include various ways inwhich the IPG can be programmed consistent with optimal stimulationparameters 420′ determined for the patient, such as: an economy mode 500that provides stimulation parameters having a low power draw; a sleepmode 502 which optimizes the stimulation parameters for the patientwhile sleeping; a feel mode 504 which allows a patient to feel thestimulation (supra-perception); a comfort mode 506 for normal everydayuse; an exercise mode 508 that provide stimulation parametersappropriate for when the patient is exercising; and an intense mode 510usable for example if the patient is experiencing pain, and wouldbenefit from more intense stimulation. Such stimulation modes can beindicative of a patient's posture or activity. For example, a sleep mode502 provides stimulation optimized for sleep (e.g., when the patient islying down and is not moving significantly), and an exercise mode 508provides stimulation optimized for exercise (e.g., when the patient isstanding up and is moving significantly). Although not shown,stimulation modes can also be included that provide stimulationoptimized for different patient postures, such as supine, prone,standing, sitting, etc., or for different conditions such as cold or badweather. While illustrated in the context of the patient's externalcontroller, realize as in other examples that another external deviceusable to program a patient's IPG can be used as well to select thestimulation modes, such as the clinician programmer 50.

A patient can select from these stimulation modes, and such selectionscan program the IPG 10 to provide a subset of stimulation parametersuseful for that mode governed by the optimal stimulation parameters420′. For some stimulation modes, the subset of stimulation parametersmay be wholly constrained by (wholly within) the volume of optimalstimulation parameters 420′ determined for the patient, and hence wouldprovide optimal sub-perception stimulation therapy for the patient. Thesubsets for other modes may only be partially constrained by the optimalstimulation parameters, as explained further below. In all caseshowever, the subsets are determined using the optimal stimulationparameters (either 420 or 420′). Preferably, the subsets are determinedfor the patient at the clinician programmer 50 and are transmitted toand stored in the patient's external controller 45. Alternatively, thedetermined optimal stimulation parameters can be transmitted to theexternal controller 45, leaving it to the external controller 45 todetermine the subsets from the optimal stimulation parameters.

The number of stimulation modes made available for selection by thepatient on the external controller 45 may be limited or programmed by aclinician. This may be warranted because some stimulation modes may notbe relevant for certain patients. In this regard, the clinician mayprogram the patient's external controller 45 to specify the stimulationmodes available, such as by entering an appropriate clinician'spassword. Alternatively, the clinician may program the externalcontroller 45 using clinician programmer 50 to program the externalcontroller 45.

Examples of the subsets 425 x of stimulation parameters are shown inFIGS. 24A-29B. FIGS. 24A and 24B show a subset 425 a of stimulationparameters coordinates used when the economy mode 500 is selected, whichcomprises a subset of the optimal stimulation parameters 420′ having alow power draw. Subset 425 a may, like optimal stimulation parameters420′, comprise a three dimensional volume of F, PW, and A parameters,and again (compare FIGS. 22C and 22D), two two-dimensional graphs areused to represent subset 425 a, with FIG. 24A showing the relationshipbetween frequency and pulse width, and with FIG. 22D showing therelationship between frequency and amplitude.

To affect a low power draw, frequencies within subset 425 a are low,such as limited to a frequency range of 10 to 100 Hz, even though theoptimal stimulation parameters 420′ may have been determined over awider range, such as 10 to 1000 Hz. Further, while optimal pulse widthswithin this frequency range may vary more significantly in optimalstimulation parameters 420′, subset 425 a may be constrained to lower ofthese pulse widths, such as the lower half of such pulse widths, asshown in FIG. 24A. Again, using a lower pulse width will result in lowerpower draws. Furthermore, subset 425 a may be constrained to loweramplitudes within optimal stimulation parameters 420′ for the relevantfrequency range, as shown in FIG. 24B, again resulting in lower powerdraws. In short, subset 425 a can comprise a smaller volume ofstimulation parameters wholly within the volume of optimal stimulationparameters 420′ that provide adequate sub-threshold stimulation for thepatient, while providing lower power draws from the IPG's battery 14.Not all subsets 425 x corresponding to selected stimulation modes (FIG.23) contain stimulation parameters that are necessarily wholly withinthe determined optimal stimulation parameters 420′ as shown in somesubsequent examples.

When economy mode 500 is selected, the external controller 45 couldsimply transmit a single low-power optimal parameter (F, PW, A) withinsubset 425 a to the IPG for execution. However, and more preferably, theuser interface will include means to allow the patient to adjuststimulation parameters to those within subset 425 a. In this regard, theuser interface can include a slider interface 550 and a parameterinterface 560. The slider interface 550 can be as explained earlier (seeFIG. 21), and can include a cursor to allow the patient to slide throughparameters in subset 425 a. In the example shown, slider interface 550may not adjust the pulse width, which is set to a particular value(e.g., 325 μs), but the frequency and amplitude can vary. This is justone example, and all three of frequency, pulse, and amplitude may bevariable by the slider in other examples, or other of the parameters maybe held constant. Note that more complicated user interfaces can be usedto allow the patient to navigate through subset 425 a. For example,although not shown, user interface elements having a morethree-dimensional quality, such as those discussed earlier in FIGS. 22Eand 22F, can be used to navigate the volume of subset 425 a. Parameterinterface 560 can also allow the patient to navigate parameters withinsubset 425 a, and is shown simply as having selectable buttons toincrease or decrease the parameters within the determined subset 425 a.Parameter interface 560 may also include fields displaying the currentvalues for frequency, pulse width, and amplitude. Initially, thesevalues may be populated with parameters that are roughly in the centerof the determined subset 425 a, thus allowing the patient to adjust thestimulation around that center.

FIGS. 25A and 25B shows selection of sleep mode 502, and the subset 425b of optimal stimulation parameters 420′ that results when thisselection is chosen. Subset 425 b in this example is determined usingthe optimal stimulation parameters 420′ in a manner such that subset 425b is only partially constrained by optimal stimulation parameters 420′.Subset 425 b may include low-to-medium frequencies (e.g., 40 to 200 Hz)within optimal stimulation parameters 420′, and can include medium pulsewidths otherwise permitted by 420′ for this frequency range, as shown byFIG. 25A.

Because the intensity of the stimulation may not need to be as highduring sleep, amplitudes within subset 425 b may fall outside ofamplitudes otherwise suggested by optimal parameters 420′, as shown inFIG. 25B. For example, while optimal parameters 420′ may suggest forexample that the amplitude based on earlier modelling would fall withina range of 3.6 to 4.0 mA for the frequency and pulse width ranges ofinterest, the amplitude within subset 425 b in this example be set toeven lower values. Specifically, as shown in the slider interface 550,the amplitude can be set between 1.5 mA and 4.0 mA. To know where thelower boundary of amplitude should be set, modeling information caninclude an additional model 422, which may be determined separately fromoptimal stimulation parameters 420′ based on patient testing. Permittingthe use of amplitudes lower than those suggested by optimal parameters420′ may be warranted in the case of sleep due to expected changes inthe location of the electrodes leads within a patient's spinal columnwhen the patient is lying down. Further, a patient may be less botheredby pain while sleeping, and therefore lower amplitudes could still bereasonably effective. This being said, subset 425 b could also comprisevalues (including amplitude) wholly within and constrained by optimalstimulation parameters 420′, similar to what was shown for subset 425 ain FIGS. 24A and 24B.

FIGS. 26A and 26B show selection of feel mode 504, and the resultingsubset 425 c useable for a given patient during this mode. The purposeof this mode is to allow the patient, at their discretion, to feel thestimulation that his IPG is providing. In other words, the stimulationprovided to the patient in this mode is supra-perception. The optimalstimulation parameters 420′ preferably define a volume of stimulationparameters in which sub-perception stimulation is optimized for thepatient. However, as described earlier, perception threshold pth ismeasured and modelled as part of the determination of optimalsub-threshold stimulation parameters 420′. As such, perception thresholdpth as determined earlier is useful during this mode to selectamplitudes that a patients will feel—i.e., amplitudes that are higherthan pth for the other stimulation parameters (particularly pulsewidth). The feel mode 504 is thus an example in which it is beneficialto include pth values (or pth ranges) within optimal stimulationparameters 420′.

It is generally easier for a patient to feel stimulation at lowerfrequencies, and thus selection of feel mode may constrain stimulationin subset 425 c to lower frequencies (e.g., 40 to 100 Hz), as shown inFIG. 26A. Control of the pulse width may not be a primary concern, andthus the pulse width may have a medium range as permitted by 420′ forthis frequency range, again as shown by FIG. 26A.

However, because the patient in this mode intends to feel thestimulation, the amplitude within subset 425 c is set to higher values,as shown in FIG. 26B. Specifically, the amplitudes for the relevantfrequencies and pulse widths are set not only to be higher than theupper bound for amplitudes as determined for optimal stimulationparameters 420′; they are also set at or higher than the perceptionthreshold, pth. As noted earlier, the perception threshold, pth, andmore particularly significant ranges for pth as determined for thepatient (when statistical variation is considered), can be included withthe optimal stimulation parameters 420′ (see FIG. 22D) to useful effectin this mode. Thus, subset 425 c is be defined to set the amplitude at avalue or within a range that should provide supra-perception stimulationbased on earlier measurements and modelling. If pth is defined by arange in light of statistical variance, the permissible range ofamplitude for the feel mode 504 may be set beyond the upper value ofthat range, as shown in FIG. 26B. Therefore, while the optimal(sub-perception) amplitude (per 420′) for the frequency range ofinterest may range from about 3.7 to 4.5 mA, the amplitudes withinsubset 425 c are set to about 5.8 to 7.2 mA, beyond the upper bound ofthe pth range to guarantee that the resulting stimulation issupra-perception for the patient in question. In this example, note thatsubset 425 c is determined using the optimal stimulation parameters420′, but is only partially constrained by such optimal parameters.Frequency and pulse width are constrained; amplitude is not, because theamplitude in this subset 425 c is set beyond 420′, and more particularlybeyond pth.

FIGS. 27A and 27B shows selection of a comfort mode 506, and theresulting subset 425 d of stimulation parameters for this mode. In thismode, stimulation parameters are set via subset 425 d to nominal valueswithin the optimal stimulation parameters 420′: medium frequencies suchas 200 to 400 Hz, and medium pulse widths for those frequencies, such as175 to 300 μs as shown in slider interface 550, as shown in FIG. 27A.Amplitudes within subset 425 d may likewise be medium amplitudes withinoptimal stimulation parameters 420′ for the frequencies and pulse widthsat issue, as shown in FIG. 27B. In this example, the stimulationparameters in subset 425 d are wholly constrained by optimal stimulationparameters 420′, although as noted earlier, this doesn't have to be thecase for every subset.

FIGS. 28A and 28B show selection of an exercise mode 508, and the subset425 e of stimulation parameters associated with this mode. In this mode,it may be warranted to use medium-to-high frequencies (e.g., 300-600Hz), but pulse widths that are higher than those prescribed by optimalstimulation parameters 420′ for these frequencies, as shown in FIG. 28A.This is because the position of the electrode leads in the patient maybe more variable when the patient is moving, and hence it may be usefulto provide higher injections of charge into the patient which higherpulse widths would achieve. As shown in FIG. 28B, the amplitudes usedmay span a medium range for the frequencies and pulse widths involved,but higher amplitudes beyond 420′ (not shown) could also be used toprovide additional charge injection as well. Subset 425 e shows anexample where the frequency and amplitude are constrained by optimalstimulation parameters 420′, but pulse width in not; thus subset 425 eis only partially constrained by optimal stimulation parameters 420′.Subset 425 e in other examples could be wholly constrained within theoptimal stimulation parameters 420′ determined earlier.

FIGS. 29A and 29B show selection of an intense mode 510 of stimulation.In this mode, stimulation is more aggressive, and the subset 425 f ofstimulation parameters may occur at higher frequencies (e.g., 500 to1000 Hz). However, the pulse width and amplitudes at these frequenciesmay be medium for the frequencies involved, as shown in FIGS. 29A and29B respectively. In this example, the subset of stimulation parametersin subset 425 f may be wholly constrained by (contained within) optimalstimulation parameters 420′. As in earlier examples, the patient can useinterfaces 550 or 560, or other interface elements not shown, to adjuststimulation within the subset 425 x corresponding to the patient'sstimulation mode selection (FIG. 23). Less preferably, selection of astimulation mode may cause the external controller 45 to send a singleset of stimulation parameters (F, PW, A) determined using the optimalstimulation parameters 420′ (or 420).

Notice that the stimulation parameters in subsets 425 x may overlap;some F, PW, and A values in one subset (e.g., 425 a) may also be presentin another subset (e.g., 425 b). In other words, it is not strictlynecessary that stimulation parameters in a given subset are unique tothat subset, or the stimulation mode that that subset represents,although this could also be the case. Furthermore, the boundaries of thevarious subsets 425 x may be adjustable. For example, although notshown, the external controller 45 could have options to change theboundaries for the various subsets. Using such options, a patient orclinician could for example change one or more of the stimulationparameters (e.g., frequency) in a subset (e.g., by increasing thefrequencies within subset 425 a from 10 to 100 Hz to 10 to 150 Hz).Adjustments to the subsets 425 x may also be affected in response tocertain feedback, such as patient pain ratings as may be entered intothe external device 45, or detection of patient activity or posture.More complex adjustments may be locked to the patient, and only madeaccessible by the clinician, with such accessibility being provided byentering a password into the external controller 45 for example. Behindsuch password protection, the subsets 425 x may be adjustable, and/orother stimulation modes (e.g., beyond those shown in FIG. 23) may bemade accessible to the clinician only. As before, clinician adjustmentsof this sort may also be made by the clinician using clinicianprogrammer 50.

The subsets 425 x may also be automatically updated from time to time.This may be advantageous, because the underlying modelling leading tothe generation of optimal stimulation parameters 420′ may change orbecome better informed as data is taken on more patients. It may alsolater be learned that different stimulation parameters better producethe effects desired for the stimulation modes, and so it may bewarranted to adjust which parameters are included in the subsets.Different stimulation modes, provided for different reasons or toproduce different effects, may also become apparent later, and so suchnew modes and their corresponding subsets may be later programmed intothe external controller 45, and presented to the patient in thestimulation mode user interface of FIG. 23. Updating of the subsetsand/or stimulation modes can occur wirelessly, either by connection ofthe external controller 45 to a clinician's programmer, or to a networksuch as the Internet. It should be understood that the stimulation modesdisclosed, and the subset of stimulation parameters 425 x correspondingto such modes, are merely exemplary, and that different modes or subsetscould be used.

Referring again to FIG. 23, the stimulation mode user interface caninclude an option 512 to allow the patient or clinician to define acustom mode of stimulation. This custom mode 512 may allow the user toselect a frequency, pulse width, and amplitude, or to define a subset,at least partially defined by optimal stimulation parameters 420′.Selection of this option may provide a user interface that allows apatient to navigate different stimulation parameters within optimalstimulation parameters 420′, such as those shown earlier in FIGS. 22Eand 22F. Should the patient find stimulation parameters through thisoption that seem effective to operate as a simulation mode, the userinterface can allow the stimulation mode to be stored for future use.For example, and referring to FIG. 22E, the patient may have foundstimulation parameters within optimal stimulation parameters 420′ thatare beneficial when the patient is walking. Such parameters may then besaved by the patient, and appropriately labeled, as shown at userinterface element 580 in FIG. 22D. This newly-saved stimulation mode maythen be presented to the patient (FIG. 23) as a selectable stimulationmode. The logic in the external controller 45 may additionally define asubset 425 (e.g., 425 g) of stimulation parameters through which thepatient can navigate when this user-defined stimulation mode is laterselected. Subset 425 g may comprise for example stimulation parametersthat bound the patient's selected parameters (e.g., +/−10% of thefrequency, pulse width and amplitude selected by the patient), but whichare still wholly or partially constrained by the optional stimulationparameters 420′.

As shown in FIG. 23, the stimulation mode user interface can alsoinclude an option 514 that automatically selects and adjusts thestimulation mode for the patient based on various factors that the IPG10 may detect. Selection of this automatic mode 514 is shown in furtherdetail in FIG. 30. Preferably, selection of the automatic mode 514allows the patient to select 570 which of the stimulation modes he wouldlike detected, and to be automatically used by his IPG 10. In thedepicted example, the user has selected the sleep mode 502, the comfortmode 506, and the exercise mode 508. The IPG 10 will try toautomatically detect when these stimulation modes should be entered, andin this regard the IPG 10 can include a stimulation mode detectionalgorithm 610. As shown, this algorithm may be programmed into thecontrol circuitry 600 of the IPG 10. The control circuitry can comprisea microprocessor, microcomputer, an FPGA, other digital logicstructures, etc., which is capable of executing instructions anelectronic device. Alternatively, algorithm 610 in the IPG 10 canattempt to detect, and adjust stimulation for, all stimulation modes(e.g., 500-510) supported by the system, without the need for the userto select 570 stimulation modes of interest.

Algorithm 610 can receive different inputs relevant to detecting thestimulation mode, and hence subsets 425 x, that should be used for apatient at any given time. For example, the algorithm 610 may receiveinput from various sensors that indicate the posture and/or activitylevel of the patient, such as an accelerometer 630. The algorithm 610may also receive input from various other sensors 620. In one example,the sensors 620 can include the electrodes Ex of the IPG 10, which cansense various signals relevant to stimulation mode determination. Forexample, and as discussed in U.S. Pat. No. 9,446,243, signals sensed atthe electrodes can be used to determine (complex) impedances betweenvarious pairs of the electrodes, which can be correlated in thealgorithm 610 to various impedance signatures indicative of patientposture or activity. Signals sensed at the electrodes may comprise thoseresulting from stimulation, such as Evoked Compound Action Potentials(ECAPs). Review of various features of detected ECAPs can be used todetermine patient posture or activity, as disclosed in U.S. Pat. No.10,926,092. Signals sensed at the electrodes may also comprisestimulation artifacts resulting from the stimulations, which can alsoindicate patient posture or activity, as disclosed in Int'l (PCT) PatentApplication Publication WO 2020/251899. Sensed signals at the electrodescan also be used to determine a patient's heart rate, which may alsocorrelate to patient posture or activity, as disclosed in U.S. Pat. No.10,974,042.

The algorithm 610 can receive other information relevant to determiningstimulation modes. For example, clock 640 can provide time informationto the algorithm 610. This can be relevant to determining, orconfirming, whether the patient is involved in activities that occurduring certain times of day. For example, it may be expected that thepatient may be asleep during evening hours, or exercising duringmornings or afternoon hours. Although not shown, the user interface mayallow time ranges for expected activities to be programmed, such aswhether a patient prefers to exercise in the morning or afternoon. Thealgorithm 610 can also receive input from the battery 14, such as thecurrent state of the battery's voltage, Vbat, which may be provided byany number of voltage sensors, such as an Analog-to-Digital Converter(ADC; not shown). This can be useful for example in deciding when theeconomy mode 500 or other power-based stimulation mode should beautomatically entered, i.e., if Vbat is low.

In any event, the stimulation mode detection algorithm 610 canwirelessly receive an indication that the automatic mode 514 has beenselected, as well as any of the selected modes 570 of interest to thepatient. The algorithm 610 can then determine using its various inputswhen those modes should be entered, and thus will enable the use of thesubsets 425 x corresponding to the detected stimulation modes atappropriate times. In the example of FIG. 30 for example, the algorithm610 may determine using the accelerometer 630, sensors 620, and theclock 640 that a person during evening hours is still, supine, or prone,and/or that his heart rate is slow, and thus determine that the personis presently sleeping. Algorithm 610 may at that time automaticallyactivate sleep mode 502, and activate use of stimulation parameterswithin subset 425 b (FIGS. 25A-25B) corresponding to this mode. Further,the IPG 10 may transmit notice of the present stimulation modedetermination back to the external controller 45, which may be displayedat 572. This can be useful to allow the patient to review that thealgorithm 610 has correctly determined the stimulation mode. Further,notifying the external controller 45 of the presently-determined modecan allow the proper subset 425 x for that mode to be used by theexternal controller 45 to allow a patient adjustment to stimulation.That is, the external controller 45 can use the determined mode (sleep)to constrain adjustment (FIGS. 25A-25B) to the corresponding subset (425b) for that mode.

If the algorithm 610 determines using one or more of its inputs that aperson is quickly changing position, is upright, and/or that his heartrate is high, it may determine that the person is presently exercising,a stimulation mode of interest selected by the patient. Algorithm 610may at that time automatically activate exercise mode 508, and activateuse of stimulation parameters within subset 425 e (FIGS. 25A-25B)corresponding to this mode. Again, the IPG 10 may transmit notice ofthis present stimulation mode determination back to the externalcontroller 45, to constrain adjustment (FIGS. 28A-28B) to thecorresponding subset (425 e) for that mode. If the algorithm 610 cannotdetermine that the patient is sleeping or exercising, it may default toa selection of the comfort mode 506, and provide stimulation,notification, and constrain adjustment (subset 425 d, FIGS. 27A-27B)accordingly.

The external controller 45 may also be useful in determining therelevant stimulation mode to be used during selection of the automaticmode. In this regard, the external controller 45 can include sensorsuseful to determine patient activity or posture, such as anaccelerometer, although this isn't shown in FIG. 30. The externalcontroller 45 can also include a clock, and can wirelessly receiveinformation from the IPG 10 concerning its battery voltage, and fromsensors 620 regarding signals that are detected at the IPG's electrodes.Thus, the external controller 45 may also include a stimulation modedetection algorithm 610′ responsive to such inputs. This algorithm 610′can take the place of algorithm 610 in the IPG 10, or can supplement theinformation determined from algorithm 610 to improve the stimulationmode determination. In short, and as facilitated by the bi-directionalwireless communication between the external controller 45 and the IPG10, the stimulation mode detection algorithm can effectively be splitbetween the external controller and the IPG 10 in any desired fashion.

Further, the external controller 45 can receive relevant information todetermine which stimulation mode should be entered from various othersensors. For example, the external controller 45 can receive informationfrom a patient-worn external device 612, such as a smart watch or smartphone. Such smart devices 612 contain sensors indicative of movement(e.g., an accelerometer), and can include biological sensors as well(heart rate, blood pressure), which can be helpful to understandingdifferent patient states, and thus different stimulation modes thatshould be used. Other sensors 614 more generically can also providerelevant information to the external controller 45. Such other sensors614 could include other implantable devices that detect variousbiological states of the IPG patient (glucose, hear rate, etc.). Suchother sensors 614 can provide still other information. For example,because cold or bad weather has been shown to affect an IPG patientstimulation therapy, sensor 614 could comprise weather sensors thatprovide weather information to the external controller 45. Note thatsensor 614 may not need to communicate directly with the externalcontroller 45. Information from such sensors 614 can be sent by anetwork (e.g., the Internet) and provided to the external controller 45via various gateway devices (routers, WiFi, Bluetooth antennas, etc.).

FIG. 31 shows another example of a user interface on the patient'sexternal controller 45 that allows a patient to select from differentstimulation modes. In this example, the different stimulation modes(consistent with optimal stimulation parameters 420′ determined for thepatient) are displayed in a two-dimensional representation. In theexample shown, the two-dimensional representation comprises a graph ofpulse width (Y axis) versus frequency (X-axis), but any two stimulationparameters (amplitude versus frequency, or pulse width versus amplitude)could have been used as well. However, note that these X and Y axes maynot be labeled, nor labeled with particular pulse width or frequencyvalues, if the goal is to provide the patient with a simple userinterface unencumbered by technical information that the patient may notunderstand.

Labeled in this two-dimensional representation are the differentstimulation modes discussed earlier, with boundaries showing the extentof the subsets 425 x of each stimulation mode. Using thisrepresentation, the patient can position a cursor 430 to select aparticular stimulation mode, and in so doing select a frequency andpulse width, and its corresponding subset 425 x. Because the subsets 425x may overlap, selection at a particular frequency and pulse width mayselect more than one stimulation mode, and more than one subset 425 x,thus allowing the patient to navigate through more than one subset ofstimulation parameters. Because amplitude is not represented in the twodimensional representation, the amplitude may automatically be adjustedto a suitable value given the stimulation mode/subset 425 x, or theparticular frequency/pulse width, selected. Alternatively, a separateslider can be included to allow the patient to additionally adjust theamplitude in accordance with subsets 425 x for each of the stimulationmodes. As explained above, the amplitude may be wholly constrainedwithin optimal stimulation parameters 420′ by the selected mode/subset,or may be allowed to range beyond 420′ (e.g., FIGS. 25B, 26B). In a morecomplex example, the representation could include a three-dimensionalspace (F, PW, A) in which the patient can move the cursor 430, similarto that shown in FIG. 22E, with three-dimensional subsets 425 x for thestimulation modes displayed.

FIG. 32 shows another GUI aspect that allows a patient to adjuststimulation in accordance with the modelling developed for the patient.In these examples, a suggested stimulation region 650 is shown for thepatient, overlaid on user interface elements that otherwise allow thepatient to adjust stimulation. The examples in FIG. 32 show modificationto the graphical user interfaces shown in FIGS. 21 and 31, but could beapplied to other user interface examples as well. In these examples,suggested stimulation region 650 provides for the patient a visualindicator where the patient may want to select (using cursor 430 forexample) stimulation settings consistent with optimal stimulationparameters 420 or 420′, or subsets 425 x. These regions 650 can bedetermined in different manners. They can be mathematically determinedusing the optimal stimulation parameters 420 or 420′ or subsets 425 x,such as by determining a center or “center of mass” of such regions.They may also be determined with specific focus on providing stimulationparameters that have an appropriate amplitude, intensity, or totalcharge for the patient. This may be particularly useful if the patient'sprevious selections have moved far away from such ideal values. Regions650 may also be determined during a fitting procedure—by determiningregions or volumes that the patient most prefers within optimalstimulation parameters 420 or 420′ or subsets 425 x.

Furthermore, regions 650 can be determined over time for the patientbased on previously selected stimulation parameters. Thus, regions 650can correlate to setting most often used by the patient. In an improvedexample, the patient may also provide feedback relevant to determiningthe location of regions 650. For example, the external device 45 caninclude an option 652 to allow a patient to provide an indication oftheir symptoms (e.g., pain) using a rating scale as shown. Over time,the external controller can track and correlate the pain ratings inputat 652 with the stimulation parameters selected, and draw or updateregion 650 to appropriate locations overlaying the stimulationadjustment aspects where the patient has experienced the bestsymptomatic relief. Again, a mathematical analysis weighting stimulationparameters versus their pain ratings, or a center of mass approach, canbe used.

It should be noted the use of the disclosed techniques should notnecessarily be limited to the specific frequencies tested. Other datasuggests applicability of the disclosed technique to provide pain reliefwithout paresthesia at frequencies as low as 2 Hz.

Various aspects of the disclosed techniques, including processesimplementable in the IPG or ETS, or in external devices such as theclinician programmer or external controller to render and operate theGUI 64, can be formulated and stored as instructions in acomputer-readable media associated with such devices, such as in amagnetic, optical, or solid state memory. The computer-readable mediawith such stored instructions may also comprise a device readable by theclinician programmer or external controller, such as in a memory stickor a removable disk, and may reside elsewhere. For example, thecomputer-readable media may be associated with a server or any othercomputer device, thus allowing instructions to be downloaded to theclinician programmer system or external controller or to the IPG or ETS,via the Internet for example.

Although particular embodiments of the present invention have been shownand described, it should be understood that the above discussion is notintended to limit the present invention to these embodiments. It will beobvious to those skilled in the art that various changes andmodifications may be made without departing from the spirit and scope ofthe present invention. Thus, the present invention is intended to coveralternatives, modifications, and equivalents that may fall within thespirit and scope of the present invention as defined by the claims.

What is claimed is:
 1. A method for programming a patient's stimulatordevice, the method comprising: providing at an external deviceinformation indicative of a plurality of subsets of stimulationparameters previously determined for the patient, wherein thestimulation parameters in the subsets provide sub-perception stimulationfor the patient, wherein each subset corresponds with one of a pluralityof stimulation modes; and providing a Graphical User Interface (GUI) onthe external device to allow the patient to select from the plurality ofstimulation modes, wherein selection of one of the stimulation modeslimits programming the stimulator device to stimulation parameters thatare within the corresponding subset of stimulation parameters.
 2. Themethod of claim 1, further comprising: determining a model for thepatient, wherein the model comprises information indicative of predictedstimulation parameters useable for the patient; and determining theinformation indicative of the plurality of subsets of stimulationparameters using the model.
 3. The method of claim 2, wherein the modelis determined for the patient using measurements taken from the patientin response to providing stimulation to the patient during a testingprocedure.
 4. The method of claim 3, wherein the stimulation is providedto the patient during the testing procedure at different pulse widths,and wherein the measurements comprise an indication of a perceptionthreshold at each pulse width, thereby determining a relationshipbetween pulse width and perception threshold for the patient.
 5. Themethod of claim 4, wherein the model is determined by comparing therelationship to another model to determine the predicted stimulationparameters in the model, wherein the another model comprises arelationship between frequency, pulse width, and perception threshold.6. The method of claim 2, wherein the model and the informationindicative of the plurality of subsets are determined in a clinicianprogrammer in communication with the stimulator device, and furthercomprising transmitting the information indicative of the plurality ofsubsets from the clinician programmer to the external device.
 7. Themethod of claim 2, wherein the predicted stimulation parameters in themodel comprise a line or volume of frequency, pulse width, and amplitudecoordinates.
 8. The method of claim 2, wherein at least one subset isdetermined using the model such that the stimulation parameters of theat least one subset are wholly constrained by the predicted stimulationparameters in the model.
 9. The method of claim 2, wherein at least onesubset is determined using the model such that the stimulationparameters of the at least one subset are partially constrained by thepredicted stimulation parameters in the model.
 10. The method of claim2, wherein the predicted stimulation parameters in the model comprisesstimulation parameters that provide sub-perception stimulation for thepatient.
 12. The method of claim 1, wherein the stimulation parametersin each subset comprise a line or a volume of frequency, pulse width,and amplitude coordinates.
 13. The method of claim 1, wherein at leastone of the stimulation modes is indicative of a posture or activity ofthe patient.
 14. The method of claim 1, wherein at least one of thestimulation modes is indicative of a power mode for the stimulatordevice.
 15. The method of claim 1, further comprising providing on theGUI an automatic option that allows for detection when at least one ofthe stimulation modes should be entered, wherein detection of one of thestimulation modes limits programming the stimulator device withstimulation parameters that are within the corresponding subset ofstimulation parameters for the detected one of the stimulation modes.16. The method of claim 1, further comprising providing on the GUI oneor more options to allow the patient to program the stimulator device byselecting stimulation parameters that are within the subset ofstimulation parameters corresponding with the selected stimulation mode.17. The method of claim 16, wherein at least one of the one or moreoptions allows the patient to simultaneously adjust at least two of afrequency, pulse width, and amplitude of the parameters to which thestimulator device is programmed.
 18. The method of claim 1, wherein theGUI is provided on a patient external controller, and further comprisingprogramming the plurality of stimulation modes using a clinicianprogrammer.
 19. A system, comprising: a stimulator device configured forimplantation in a patient comprising a plurality of electrodes; and atleast one external device configured to provide at the external deviceinformation indicative of a plurality of subsets of stimulationparameters previously determined for the patient, wherein thestimulation parameters in the subsets provide sub-perception stimulationfor the patient, wherein each subset corresponds with one of a pluralityof stimulation modes; and provide a Graphical User Interface (GUI) onthe external device to allow the patient to select from the plurality ofstimulation modes, wherein selection of one of the stimulation modeslimits programming the stimulator device to stimulation parameters thatare within the corresponding subset of stimulation parameters.
 20. Atleast one non-transitory computer readable medium configured foroperation in at least one external device configured to program astimulator device implantable in a patient with stimulation to beprovided at one or more of the plurality of electrodes, wherein the atleast one medium includes instructions that, when executed on the atleast one external device, are configured to: provide at the externaldevice information indicative of a plurality of subsets of stimulationparameters previously determined for the patient, wherein thestimulation parameters in the subsets provide sub-perception stimulationfor the patient, wherein each subset corresponds with one of a pluralityof stimulation modes; and provide a Graphical User Interface (GUI) onthe external device to allow the patient to select from the plurality ofstimulation modes, wherein selection of one of the stimulation modeslimits programming the stimulator device to stimulation parameters thatare within the corresponding subset of stimulation parameters.